{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LSIM-PITWYFa"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/jeffheaton/app_deep_learning/blob/main/t81_558_class_05_3_vision_transfer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YDTXd8-Lmp8Q"
   },
   "source": [
    "# T81-558: Applications of Deep Neural Networks\n",
    "**Module 6: Convolutional Neural Networks (CNN) for Computer Vision**\n",
    "* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n",
    "* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ncNrAEpzmp8S"
   },
   "source": [
    "# Module 5 Material\n",
    "\n",
    "- Part 5.1: Image Processing in Python [[Video]](https://www.youtube.com/watch?v=Sob7VDb4xh8&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_05_1_python_images.ipynb)\n",
    "- Part 5.2: Using Convolutional Neural Networks [[Video]](https://www.youtube.com/watch?v=jL0_lOpEwSk&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_05_2_cnn.ipynb)\n",
    "- **Part 5.3: Using Pretrained Neural Networks** [[Video]](https://www.youtube.com/watch?v=W2T-dfiHYSo&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_05_3_vision_transfer.ipynb)\n",
    "- Part 5.4: Looking at Generators and Image Augmentation [[Video]](https://www.youtube.com/watch?v=20JoEmQb810&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_05_4_generators.ipynb)\n",
    "- Part 5.5: Recognizing Multiple Images with YOLOv5 [[Video]](https://www.youtube.com/watch?v=7Uu1n9Tp0Mk&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_05_5_yolo.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lux_6KOXMU94"
   },
   "source": [
    "# Google CoLab Instructions\n",
    "\n",
    "The following code ensures that Google CoLab is running the correct version of TensorFlow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fU9UhAxTmp8S",
    "outputId": "a7dc7035-f775-4c3e-cded-e9cce17207e9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: not using Google CoLab\n",
      "Using device: mps\n"
     ]
    }
   ],
   "source": [
    "# Detect Colab if present\n",
    "try:\n",
    "    from google.colab import drive\n",
    "    COLAB = True\n",
    "    print(\"Note: using Google CoLab\")\n",
    "    %tensorflow_version 2.x\n",
    "except:\n",
    "    print(\"Note: not using Google CoLab\")\n",
    "    COLAB = False\n",
    "\n",
    "# Make use of a GPU or MPS (Apple) if one is available.  (see module 3.2)\n",
    "import torch\n",
    "\n",
    "device = (\n",
    "    \"mps\"\n",
    "    if getattr(torch, \"has_mps\", False)\n",
    "    else \"cuda\"\n",
    "    if torch.cuda.is_available()\n",
    "    else \"cpu\"\n",
    ")\n",
    "print(f\"Using device: {device}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q09yMGGcmp9N"
   },
   "source": [
    "# Part 5.3: Transfer Learning for Computer Vision\n",
    "\n",
    "In the vast and intricate landscape of deep learning, getting started can often feel like a monumental task. Fortunately, with PyTorch's **torchvision.models**, one need not reinvent the wheel. This submodule provides a rich library of prebuilt and pretrained neural network models, streamlining the process for developers and researchers alike. While these models can be deployed \"out of the box\" for quick tasks, they can also be fine-tuned foundational structures for specific applications, offering versatility for a broad spectrum of tasks. Especially in the realm of image processing, torchvision.models showcases an impressive lineup – from the classic convolutional architectures like AlexNet and VGG to more contemporary structures such as ResNets, DenseNets, and MobileNets. In this section, our focus will primarily revolve around these image-based models. To elucidate the process, we will delve deep into a hands-on example that harnesses the prowess of the models.resnet50 to craft a state-of-the-art image classifier.\n",
    "\n",
    "We will begin by using the ResNet50 prebuilt neural network in its entirety. MobileNet will be loaded and allowed to classify simple images. We can already classify 1,000 images through this technique without ever having trained the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "jpN-M-BScz5K"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.models as models\n",
    "import requests\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "from IPython.display import display\n",
    "\n",
    "IMAGE_WIDTH = 224\n",
    "IMAGE_HEIGHT = 224\n",
    "IMAGE_CHANNELS = 3\n",
    "\n",
    "# Load pre-trained ResNet model and set to evaluation mode\n",
    "model = models.resnet50(weights='ResNet50_Weights.IMAGENET1K_V1').to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we dive into the mechanics of using the model, it's imperative to establish an image preprocessing pipeline. A preprocessing pipeline ensures that images fed into the neural network are in the optimal format, size, and normalization for the chosen model. Here's a breakdown of what our preprocessing pipeline entails:\n",
    "\n",
    "* **Resize(256)** This function resizes the shortest edge of the image to 256 pixels, maintaining the aspect ratio of the original image. The purpose is to standardize the image size while preserving its content.\n",
    "\n",
    "* **CenterCrop(IMAGE_WIDTH)** After resizing, the image might not be of a uniform size for a neural network. By using CenterCrop, we extract the center part of the image with a width and height equal to IMAGE_WIDTH. This step ensures that all processed images will be of a consistent size, suitable for our neural network.\n",
    "\n",
    "* **ToTensor()** Neural networks in PyTorch expect input data to be in the form of tensors. ToTensor() takes the PIL image (or a NumPy array) and converts it into a PyTorch tensor. Moreover, it scales the pixel values from a range of [0, 255] (standard for image data) to a floating-point range of [0.0, 1.0].\n",
    "\n",
    "* **Normalize(mean, std)** Normalizing input data is crucial for the proper training of neural networks. By using predefined mean and standard deviation values, this function ensures that our image data adheres to a \"standardized\" format that's been proven effective with pre-trained models. These specific mean and standard deviation values are derived from the ImageNet dataset, which is what many torchvision models (like resnet50) are pre-trained on. This normalization process makes the network's training more stable and faster.\n",
    "\n",
    "Below is the code that implements the explained preprocessing pipeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define preprocessing pipeline for the image\n",
    "preprocess = transforms.Compose([\n",
    "    transforms.Resize(256),\n",
    "    transforms.CenterCrop(IMAGE_WIDTH),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Incorporating this pipeline ensures that any image fed into our neural network, irrespective of its original size, format, or scale, will be adequately reshaped and normalized, setting the stage for consistent and accurate predictions.\n",
    "\n",
    "To proceed with image classification, understanding the output of our model is crucial. The output of models like resnet50 from torchvision.models, when pretrained on ImageNet, is typically a 1000-dimensional vector. Each dimension corresponds to the probability of the image belonging to a particular class from the ImageNet dataset.\n",
    "\n",
    "An intuitive question arises: why are we sourcing class labels from a TensorFlow site when working with PyTorch? The reason is pragmatic. The ImageNet dataset, with its 1000 classes, is a standard dataset used for training deep learning models across various frameworks, including both TensorFlow and PyTorch. The labels and their respective indices remain consistent across these platforms. Since TensorFlow has conveniently hosted a JSON file with this mapping on their storage, it's a readily available and reliable source to fetch these labels, regardless of the framework you're working with.\n",
    "\n",
    "Let's breakdown the provided code:\n",
    "\n",
    "* **IMAGE_NET_CLASSES** This is a URL pointing to the location where TensorFlow has hosted the ImageNet class index in a JSON format.\n",
    "\n",
    "* **class_idx** We use the **requests.get()** function to fetch the JSON data from the provided URL. The .json() method then parses this data, resulting in a dictionary where keys are the string indices of classes and values are a list with the class ID and the class name.\n",
    "\n",
    "* **index_class** This piece of code rearranges class_idx to get a mapping where integer indices are mapped to their respective class names. This transformation is useful for direct look-up after getting predictions from our model. For example, if the model output has the highest value at index 5, we can quickly reference index_class[5] to get the corresponding class label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the ImageNet class names\n",
    "IMAGE_NET_CLASSES = 'https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json'\n",
    "class_idx = requests.get(IMAGE_NET_CLASSES).json()\n",
    "index_class = {int(k): v[-1] for k, v in class_idx.items()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By the end of this process, we're equipped with a dictionary that will aid in translating the neural network's output into human-readable class names, allowing for intuitive and meaningful interpretations of the model's predictions.\n",
    "\n",
    "Next we create a function that will classify an image at any URL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify_image(url):\n",
    "    model.eval()\n",
    "    # Download the image from the URL\n",
    "    response = requests.get(url)\n",
    "    image = Image.open(BytesIO(response.content))\n",
    "    image = image.resize((IMAGE_WIDTH, IMAGE_HEIGHT), Image.ANTIALIAS)\n",
    "    display(image)\n",
    "\n",
    "    # Preprocess the image and prepare it for the model\n",
    "    image_tensor = preprocess(image).unsqueeze(0).to(device)  # Add batch dimension\n",
    "\n",
    "    # Get predictions from the model\n",
    "    with torch.no_grad():\n",
    "        outputs = model(image_tensor)\n",
    "        probabilities = (\n",
    "            torch.nn.functional.softmax(outputs, dim=1)[0] * 100\n",
    "        )  # Convert logits to probabilities\n",
    "        top5_prob, top5_idx = probabilities.topk(5)\n",
    "\n",
    "    # Print the top 5 predictions\n",
    "    print(\"Top 5 Predictions:\")\n",
    "    for i in range(5):\n",
    "        print(f\"{i+1}. {index_class[top5_idx[i].item()]}: {top5_prob[i].item():.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We try this classification on several different images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/ys/hps4l__90n3_2gns3lzbt9fw0000gn/T/ipykernel_26616/1839044210.py:6: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use LANCZOS or Resampling.LANCZOS instead.\n",
      "  image = image.resize((IMAGE_WIDTH,IMAGE_HEIGHT),Image.ANTIALIAS)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 Predictions:\n",
      "1. soccer_ball: 89.91%\n",
      "2. honeycomb: 7.12%\n",
      "3. whistle: 0.61%\n",
      "4. digital_clock: 0.27%\n",
      "5. golf_ball: 0.20%\n"
     ]
    },
    {
     "data": {
      "image/png": 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UZVIQ5Vl9N2fGAExZiAjF6rNhUIGCLDjeBMypjAbcGM0hzB5JuD3WykQ5rIJwfczzp3Kt3+5Ykqob823HrQi+Z8ezJe2v+2zfO7Cyh/HJ0R3I2Ig8KdkVb+acb/9NmtdALXu0eKMNYVP1mMwNGNaCCtV00N8HjiDNihc82gW3/zoFNBeNZw3Rw5G07R9mmZMTJiFukdqf1i5NJDCo8fkP74QWJVq6UI27FRHm33j26+vigd1qJa+RKQbmoKoCM1U2cNsPDDIiY392NUL5nt+hfCKeo6XbH5EZPP9ztW6F+G1RzQxNz9Au30Bw1xn2PFdLBWZqdEeu1A+fv9v3TDOemXekYxuFi1KdaXFDaNX8+odkpsYPGej+N98nbIPvrPaYG3jTSh5hhqztEmscajunxXrMLqY9YW6MhOv9mBkKAs0XsAsF8BDR8K4XUJu8Zv9lu9djDkH+0W2z7v6Vdwnobo3d47LtXTZXzKROcFK7bndtXEsP1TU0cNh8sVVrr8wsQWaKpQU4gNTUVIXYNIuIAVlLfbjcvIDB1MSpj728B7Xs4SHGsNux7PovCQGVzt5jEhu70jxI2+GAUdWDSssKffn2ki8XW7jsY4Yj7gc9m8EuvnidsX6qqft4AMSzL25/TFUs7kTS/lcrNNdwOd/DXgDdJU4OSIgUAFPVT827s+ljdylkfZp/aj+4xYgQkVTOdS8z9TcvWhSVbKOWVkrVFXz/1mpvWZNwETFrj6+9cH7VLglzCLeTm4AIxFTKLs4Qg20uVbgHqxTznsUTKmE536zV8Niwo6FhN3Yy1qMtN/04GZOSC4mpuJQTXmswpirSqO7LwEaqykHmT5odw+xrvHLrwcRFAs2pk2mp+eKOFDMFW1XUw5r4wRndhkTNSGp9vKhyTSKanaoyDFS3uF+FNftorktRMVhNXKySU1ysYGaNuLHQcGEAFWviBsfUACqkad6XKsFrZkJck8MWWcxsJmB2ZSCrAH8Ox2RGoAqzqigdkYmKKZtRaUmCmRoLBKJZa+Ix2/q+c20oxRUT4pikphsGIis2v6ZiHNRqBwASB/S78iNaDlk/a0/CT5gBzIxe6/KrF1zUeQxq0c1XQ2kuYntvBoxIoAoyQyYGQVQtSlBVUgXXR9x8LbvLNBCRkBbIajXeNFldcJRpBCeGFKo6JncvRPXquRaizCFpbVehUEw5EBmsaMXClM00DMMQJPRdJ0ROzKIBKUdslQ8X98i+veoGKkUBJ072wlaL954FtXytoqj6/ExdrDXvb2pKWF/HMMuYK3FNsz9q1mDk6YLWbonZSnjPR9bL3bltY3oItvgl8fz66qX8Yncm3twmMCsNK1iaacAZ4xDcz1Z9GcEpSQAtJtDuG/ZNSe29qBVy54SjMTBkqHLjudUEqJVGVC/qeiFQ02VSuzitm3/X/rFztL4lXEJefY25BoZBENH9Za/kdoXQBI0hnl68//mPr967Q985GZaxLwCbGAxW3P3MwIPI2MhL+CSsLgklIVLHBlxTfdSkiCUslouS8jgOwhJidI2F17ulhn6YmbQ9Wwv5qm4fqkoz3DdrpBasAlBzHYMZVGvHB7eUxWoRlWrOXd/DMx6PLdXrtgKuFyTIfM1mQZ3TE3MQrK6pPcJ9y3MR5V55YEaifn9Grlp16n33hs0YAdm9JeB/0IBcXSwQqvysYB9TtlChVlGYwaqGranf9wP/nleeN+r8Gy/lmjNravPd7rlVo0A1kliDDTu2oULhPbQzW7Y5rtjfwdZKFVqzf8wiKWam6WK4+8C2V3XoJo4qypaLGCurKeEhPkfVmAmqOSsJe5rGBFKFaqluiECsygYLwtItghbTksdxJKbYdUxMXG2RZudkbQH28NZDLS/N9TbAZ7MDdhzmBPV8z6iywl0e2uy0oqWd6Zi6AkPhhSEwyYxNAXVacN7kQMv1DSxEcwHaL1Pr1VpNHjB7TCMy9lZR7F3n7oK5la/8iueyagPEvhUq1+MKrLps8Iuo1LK/D2bhimIu6++bZo0GJO5NG6E4ByIjchnOjFD30ofKqHDLlirWoT1Ku628M7UGVObBKlVZ9erEhiZ6rSSvQYi0lHFcnz84fW3z3sGNH8yFBAmcSdUIpfimNmvPnZmhlqwQQ6FIZOBiGimYqVlxIYyXcQKTFg3+4JmZObJIKmUcRyHu+94dpHfN4uF+rp2vopqN7IpuBlAlzzyOAxBh//fZXmuMqwGqMT5zPtgQqDsJq7WlFsjqn1D16C0BmR0OGpFeP4Dqj6X510YbmVUkBjYy8lxwFwr3HAyLcJOV1XvXWqDb5Sc1hzN4uwMYKNU1srX0Z4/f9dKrp8P1g5p5elxu3qFm5UyEhyptO0W5PwlqFB6aLsJjlm8Za/2JrlKcTdygHqxmOsztlWvTRnW3cKaJwURaNOecpsnAN65f/cyHr//ir381PvOJ46MuWZCMQtQU+3vkypyxkWnToBs5MAZMjaBw3pOCFQChZvu+qCK9SI8upzwNg4iEELoQ3Dq/T2pKrZTMIDWdHa1VorAhQptD9w7sa0Nd3kNoMyikymU7DWtz4Z7Ze9+5MQwG45aPOQKYqXJq6WtpDKWiFtmkZTxEZCg1sDaCHpUttBlnWk124dVq5p0xWmuvaQQNCKTNp9a7qmyCAWY79WJdQVT7gIPLFg6cCbFi/uxaJbd647lE7GGaiGC17mVmBjVpTaQzXwADowH1vZLIzhznXmdU5FAdh5P8zjqZgTgwlaLTOHoFVaTvl8e337z1c//lF6w7TLj62Mcf2QxEVDwHp9Jgzp77cvlYrQNpcT0fmqqCqzVWHxhaKrCXNgBdF6gLOZdhGJgpxi4EaYGx/qnCPHpiNqNmvu4KZl/oMHzuutx12LS42WBB2100R8g5TdknIHabBGZCVKzmloBPQIAZnFauNr+H4YjYapeumkGq6ZuH/Nl+2sfVFtv64/4N2ve9kqgVf+e0rLr/dgvz/5vvob5P7ZjxdzTPGDwx2oOnrqYyNfXmXdpzgS1Rr5fEVWGNvVERLSVqPmIvvtucuu2QQAU/FSTU36huN2OI0r5CogyUu/fuHoCefu6ls6OnpgJhYpACArIGGBz+wlpIm7M0A1nt9N1fTPj2IIRdlbwaX71qYo4xiJCqpZRynkRCCIFZ5qIDe8tVU/3bnitvWkrHO/sA3xpIr5C5tjy6sKO1/LXkpVKX887Aw19EVUi0Q6546Bu0LE+oBftaQGkNiWZMpGV3SdweDhG8VOmBooXf3Q55aBEA+NSTXRJaF3Wfzd23XTRNfrUzNymqIIGqJzEvvAAgASuYg19z8fQUrl2u0wkq5tttA2Pefy7NLNr1zDKU79v/dR3qvrFpGv3aYheji1rqxBQC6dHR8pFw48rjT9vi8aLJl9qMvHOhmVMNuaQzJePTR/x/FfvU5wkDnBBEEG5W5B/pl8uGWq1H14UYRVVzzsOwjTGKCDP7ABlVZVcvEmxXCq/mDqpBXKvMp8ZfR2++QIVARAFNNASilog0ALSz+jlfbg5s1mfuQuecLpgpVTm5NWhYses8v8BgYiqBi5kzvg4kjKANTtTCrUOPmXyaL8Rq9Rx1k8yBveq5qluCgUiVH96ufnstiWl0hsFcFKpFG5Ux4/W60cQTw6IEQ8sYq6t6OKZVc6ifherJyGXe3xeaakZHgJpqKXXEBihIYGF4blevxZhhTIer+O/8+X/7W/fo5gMsiRhezwwOIouW3Q7xJSe4GAZFa/tBDRRUUa/VHUKgMO/1ZsJelWvupAmemXmxWHRdKaVM0wCDBP+S/e24g/+NDfZVF5fzaPMhexcsNBsOWjowP+Z2T9bucdd/W9+KiMR2EsPa8D4TAhXMuq8yNZO5ARCVoPA4yDRbZrU8btRRbZafd0V7kFbXraoTgSovJziNBK+Dtr1KqBqmh9yYqYqQ6S7gtmfTrBcEBvsuYKgZi294348VJ9I+0qtQXtmLt1T5VCYiUFFXwmNOFfbyy1oNzzkDpjmDqIsdE3Ip5lMBKmj3MVZgouWqh41mB0JERAJkUyKd8+/qUYkIFeEaalMealXH1JRJHPy21TYAYX4e1NAGtRg5g5WGuoyJSTiuopmllIbt0PUdMwcRa6+vS2yo6oq90M8tb3b7cyhbkR+5SJsQZldp2BN/MLOiFhHmTW9VO8MgqCrP0R6zwXsyq54giH2f3nQH4/bp/fk31WGZVY+8x6360prVXb+HcKxCz73bn5dbaGeFcMdGNc9qf7UDbTvZHhE8f7ayk5LATdCfb9hBiLa1tXbWN/toyjdq7fymXksHM3tnupmpljQlAH0XQh9bHDN3Ze4BYSCvGZiyMaKocCpdICVGKKQsTJk0tjIwUZULGjFqsd17x3NxfQUrqHkTcycLhZc65w1dE4/mPLS5E5pxu+c6MDLqFn2/WKSUUs7jOLJIjFGYZ+JjfofmK4G5SIsK8mzvae0o1Qp9Gnypjr0B2zlD8jfVekNURcoGMxEq1YgZmFv8qtxph7n3vdTeF9UIsIt9vi7Gu1yyOunqfXm2e2/9oxoKW6ZYr5A8jZopDp0ls14koApIuXV6OPZqA4F8/kB91UPOr7ZmVQzkkJS95lB7AdrGc0EJiAABCxMBpRTASikp59h1/WoJZ3e1tsEQSIhrL57X/BS+sp7jOIgHil+ncCioAhquTecOSVkJqu5AYZ5nm9eXZoYNBuM6N0dDi3XYxYf2vbSGtRqf9iwDrpZl6boYQzCznHOeJmU2WBc7R99o7On8jGeoMw8Am0sa++B9ZzJu1k7FgGb3jz1n5s9sdvP+W1Krm6nBFVVrYoCHTPL74Nqeme6b8b/hpRWM7XViEc2q3BruDfC5fNVr8AyHQUTCpFpQBUGtIXiWlTQGwsufFaOh7e4m+fAg2aDt/Ix9VfbwX/WjZqaBndjOWoyAlBIxdV2Mfagok2BqLAxQlaK7b8Ou9E9g8hRTs+NREIjVBzuhAo3ahmutoYVAIly08pL+R6WGo/bsas2EAJ49qLeuPpTne6yYZ1sy1UAg1KTrViv7zLRYLACklHLOuRRNqVIRIVj7qtqXGkDbbVQfvUtcMLvtKh330rOB5Puy4PYs9tmTijVCcPQMqYjNAhOIS9Hvs8iH321nhESziqCCSG52O8N2M2MR2ttgc1iXVjt1PzT/FRGYZR4/Nm+GvUTCmQ74EruZKpmiepW6Ph56HdK1dWsLVW2I5aFbC8JkZMzDNDJzyYUMXYxdXOz2nBft1FDlvzqvLXvg9RuEecVZWErR7XZQLT61hFn88bXpS0ZEJOItDKGOSrK5BVObBMYXRKE+4MhjTiChomASokJWiLxZSmEKqsmjd3h1VrrARpQ1T6WQhBbwiyMLA2KQRScp52JScp7GlBNLkCCh8ZSzg6QWSZuFNgUXdpfLzeBCw2qoe7lmUHPiUgXezcXMda8a5oioFCWYp2t7I0KtWTmaG24bZz+++/W0RGnmNtsf17BujQP3iF4RmLgjV569a1PFF93l2w81P6FO/zO4XIwE5E9kRuENY3sPXENw3o2sAFXVJzG7pgdATilPJoFNVYLERUc1KW2Sepv3inGdXrNXRakzYPwCQAAYxCREqlmJ2OufQvDpTOQ4oC2sGYNICaribatmqDpoVKxgEGICWe2mRmCbiAMoECAgwJKpIkSB5WERpOu7zXZISpdjfvGpR0GcyW6fbN66e555BQFbgnHTv2kx5cCk1MWFqpZScs5DTgSIBBHhVvasD38m6jyo1QtrO9pm30AMpVoG23N33odKMKM6WYlohptob2Y1j6kSllZn3X8v7Fuq/8l+g+zs1PaDTNWi7n213zdfXzcjYFSbIZs/hoI8lW1qr31hg9NRttseFrwMOyNnaquoVUZaoY7VZnnHGFq05Jxzdn4jxBiiLCR6i3rVqzSeEbUh0N9M2vyH+X5t76ERVV9hgQhFCxkZC8lkBjIBZapvOXMu9S0aCJemCvILFxLv6LcW5gGElWAoVmAMISYtRQhsiDk9erR48voRablzkktYHkg+OpAolMcxHEkXLt88TWdj9k5m1cwgEBcwQC6RFhZmjiEaTIsWLWma4H0vLN46vBcwW3SzeTnsoQWq3sefT+2asF2B3maTMnvIhmdz2+k8sIuJ8xWgPQGieewldi/7U8jVWpLsL55f6RBu553hHpVBVIc31DuzFqppH9zvbHQP9tQGa7O9i60pEbEAbFrIwOIdBlnNSkHOWViYqO/7KocgA7TVQr37VIAMVH1+/RB4X6ZDTLcz9n9UdUVDMzqAgJwSeVsSFV/ulk+joclm23X1anjwoTK1vFx3stVhAqwEhMeuXr55/3ybixFTzquOuxh5Gp6+cfXRo67jDOKrR1eS8cmY72zHoFOgIiLXj+NBlPdPpm1m0tT1XVacj4mkV0CgAKmWOiwHCEEMTH2npaSUtSTNpmYcIjPPims3NY8zvDd0GKilcG8T3nuYOytgbi5rdgkNy1LF7DTbltnDdmG7aNue/S5Rm7/ZfWB1w1DYniVippWcefXOh2aDO4EmKsWx52brHpuTxYd2SEP9jSitBIhvvEYAl1JyAWGaRpEYSEKMIqHdn9+zGYyEmJgI3pffFOY2+wCr4wFtduvz54twKXVIlqGSolwFZWZ1TDiT5tlCK+iC6yLN40Zjm4zaeAGXwBuTkWs8GEA4tM3lWJYxsFlv9uiVftVJNj46DEolSwhdBPT+nbtpPfaxhwi61ZjzOG7F8MiC1CAUYggT4vunmwdjIg4KAEpSMVMdKevXw9QvOre8Uko2pDQBYBGYBQki0vTLbqY7DS9a6oeWP7vbpZ0FzRzuzoZrfxlVgNCeOu0Fr31cUf3lfmGsUV72fb9xDDj76yaT5aJWyCejaH1ITvdYG8pXvay/x+xb9vw6PbR92sZojcwV0nEpeZwGEImh5NKFTiTIsnP0TS3/q2YtXCp7W6dBVQ1jpbF0TuSYxaxQpYT2dp93twqjcVUAhHnV95yZ2KdaEBmYyNs45oVuOeKe2G8GS/UJeIMDVI2EFaxAKCUd9+h7isxQ6nvOORnTxfZ82fdGhZTO18Od2w9WMUYKZHa23njVUsEFABUhIk0BetQha8mwCIIhsGzGKYOzBGOw5ppCmIEEJCyyIkurnibrk62R1pbDOJJa13ctd5I6/Iy4+OAYf1S1Zmfcer+0LUJTeJnTee5yoOrqv3bgRmnPT3ZedKfGJBcZUtNoU6X/2mR5VUIV6VmzKm59nEJovh6N/HGTZJuVkWZSt4g/pFoseHiOVx3s6M9Y1OdXqpk591xVkiJMxAtvzvSMxwc8oZ6v0Mr0/sZMwedoA2pVAEDzSDgGw7wDeJ5S4JZqDkm89qusahAYqXarPpyaAmI2MRhGQtKwtZrNgd6ohRRuo38AMVYtBFfEeKuxd8JZWI+5j8E0FyMQrcdRDTBlNtUCLSF2ahJjXI95zJsY2bSsVssYwjSlNIwMKoteYshjguYlWxfkiWuXV8tF0Xy+Gc/GfOtkPSpPHD00BWhAOhBlK8UW65KZiYUXoWPWWDRlLWo5J6oFTBYRIhWpvNiuGlQFbrW+oKrU8sbaYeVqfN+x9fASf/AP+c2ZuoO3cVX3uXdKBoShO3azNvCyxzU3s91o7Tns2vzm5sop33WN1ddGZruOrpZhfSq0qglLKRmgolpKFpGUJgLHGA+WKyMwifP4pajtzays9ZjGlTY6mWaQM7+SWjpYSjG1IAKrBlT9rhcjKolpgNej2NOrCNacxzQ5ZhACGCgwKIGJ0YYrVvP3Y4L8iWBXDCJh9maWOhbMYAIQhTQpSiazaRq6vj88PJzydHZ2wYTlso9RhjSut+PFZtACQwoTxUCSAjFP46jjSBKJsU3TdhoIdMDUi3WsjIGlXL3El8PlPnZvvneX2ZRIDSLCWgTMVlIuEShMUzCDSCGiGIJ2TLRYOF+Yc/Y6xzxeS0RcZe2up8Y/p58IvkotXDnMrznkHraTOn5vJk320CZ4N5mhVd7IIa6389VRIHvRa8cnVGz2kNTNo2mVPLfJecK1BdCqRKLATLUokKY8jlOMEYCIEFHgEIJ0XTAzKz4rdbZFw+yi9ki6tuV2YEbabuT9rI4MhCBkiiofUA1cm+WptVQBlj3dk7pabADKcrkIIagpczArPHeVY05D98B99RHakibXvGoN/XNLQdv6AZYWi4NLx4frcxwerLpFP54MpWA9bVX18OjAgIv19mI9EAfACMpMReliM+WUtEDKNJ5pTjnGsFwu+hCDyJDz/fMLlz1Bxj4srh/3gYiFJUSF3D3brMesvFr05UrsRysn08TGMBoIwjBvtqWq9gdFUyPinLN3jZU0Ob5iFiIyIhGZxcdSaTcP2xX/+kOaq1yeF0lNT3Z+FICRKHZCxYoaqQoJGC1Ph81JUnNX2JeA7kktSbWIuE8tzC7IgKp6KqKqJWcDRESYF4tusewbQdFSnNbfw4FLHXSKOcjMNNBsDC3r2tskDf/tTycmfx8mMLxXzoj/dJJa88LGiznJYIRSSr/o+aLdL7lAxGfgewZijTKGgFSNZ2FhdQukWoiUBVTP6EEwM1i4duVo0XWCcnSwNNNhvYnMy9VyzNNUyjCmMeXtMCoIWWHq+DplVYVrQ4FyWJaHy0UUQVHqaZsG5AFKQoIC0pzTveOFFOmuHS2OFt2Ucyf87gNdl3Sw0MvEIcagpY9RNbwzrkGxDa4H4GpR348lRjZjcn0Dqjw+55JyNrNSSikFamQWu67vuqKlSsZBvnb7KQhRE17uyJTZ89aBda0Apg4RXAjCZmRgQyGaGwC4eh2/Mn83BSjnrFV1ZqWUZk/VOFyZ2sUOsav66MpQ2myaNW13LAsACExFCygA6mxspd0eMtCHMj+0F/mnEFEt/raWQ/+Dhp28g9PmLIdFCH6GA4GonvYGTClZMWYRCWR59pYsjvx1htdujC7phLXc0FBlmQ2kVrYJIFBISa1so5ASD8UC0SLSwZJTjufrcXO6uVhvc1F2WZzVqnHKGWohIgSC6bKTZc952majcaSpFGYSo1QGIUZGKRmlWMD9k3wC7bpeiG5cWlyst2lbHuDi6vHRlWUolovg2lRGQjKalCG9GcRShGWGmg+MgxUVDjAmKLP1PS/6qKawzkCmxtCcUho2DgmNpBTztEdNRcSP6CMRKeqF5b3K0LxwEB8k0Z6wWR0UVEzn/AlEOWeidmKB8zmof+cJTGAP+tQF/7ZpFdz6fI+oFXNVl6cz1BjdZndGOzZNVYgICqZcz7cAiNSKwghMpXmnWk1QABy8guVIg1xQYvsjg1C1ZDOT617W20eFCCxqBlNSCMBkLKQCVSUom/csoc7JUSPi1lbsFHGt5ps1ToWgtYZbkYM37Xqrd7h1ukEeOqHY934AwSjIjGHM22Fcb/M0ZVWwCIAQBAXUhCpZM5UQWMYpgcBMzOF0vU2lMEhAyy4KUyml7ztiEUIfgpbi/xdFHrl8dO/kfNyO22HLQtNUEDQS9SiI3anqaKkQZ5FCRQsTiY/eZBgsC1QlZE+Nawsw6hAU7oSlWy5md+aD1HzvmlkwV/mSuoDDgWpjng1WzJhm9Y2z97XfmWmvKkgkgWLw7q56cGMIAmqHK9Sw6j1hMkfMXblVizWW1sjg50TU8dT+l1VEZ7QDvCDxg3IMRBYA+FILB9ICkpmpbCObyWlkzEQE3F6wDw5mJV/j8RrHYfV/7UqJiKjdLKMOJgSBXb1K5N01NhcDRWBaRZW+xi5xbZSTNZqtCtfVyCxsNWiRzThhW3LRQLh80Mdld7Edt2Mpat4QqwVgpFy8hspMQaghelkPeUyFgxSbppwIHAkhBmLyONAT5TRRpj5IjEFVAzNUhdKVSwdDz8Nm04e+iwKC9OGgU468GDAZhimrUhC2YGMeCklWKLGCslPH5EfS1u6FNgKnqClDfLqkEAhQIpm3qlXgmUUqVW2tigIUmrVwukOT7URNGKymoyimXjjeOQD1oMwNApKZzaOvieoxCaqlsVo15gnx3ng3kDOCNdj5VzGYmRCgZl0Xckl5Sst+AbNxGGPsfMugTUOvC1KTQR9t7xN7vPMWLA+3pVkdoiGQSmhV7qkp8PfKS1TbPn1gkzGxkDYGUBt5XT89GEBsTTDkH+RbQVrb0A6aOA8NCspRejYORa1Ac5pON+VQSoiLrFPRijaQ66cRwfUAgSkIiwixaMlDUqQsLDAyyxYoF6yHHEKITNtxiixEth1HpBxjJDPyIwdQhNH33WLRwYiJzRA6yyWxpRWHo4MAtchspBejKoUh2cU0ikSJ3TRNPovKirL6KStaZeFkjn8AMnUhAqEdKIGmqxdDq/dU3ZHnPFzpTc8tTHOBj8SvUReukKB6apI/MTbVRiftvqr3Im7NsQU063O1ygYYMONK71o7EbNtp5qpsXNlWmzVx69+9Q/v3b/z4ZdeePWNNy5fuXpwcHTv/snl6zceu/H4lIvjZOAh9RbJ7rwhXw13ijMDXP0zk6kR11SmBY16Sqo1JQxznVE6jWMjh5TruS1WidSy1yVTS7V19g1aYmqte6EBJGUWf7vAMXIhi8QkwXhI4/lmPTxY55KzQQ1CYmbmc20JWqwTcW7L5yuRFVWoEQMsJIAxWz0FhcygRjkV6YKHSs15s92ulguGKTQXSzl3XbfdjprzcrkMxCXH9XoYxhQ6BINauZg2OVspJQSLJJcFxONC8kXQjAVZGVHUpzxaWQTuWMZiQ84GqZHDTFGMWFlyUSJSMjMVRXUyaKQ0u5x01/riTkJnWqYOOK2R0JXwdXEbV0/MVuoYOqZ2wMhsai1R87VttDo1cXat2tdDEVrC5IdwgoigIsTAjevXX3jmmfPb7z375I3+4Orx5asSOy2FuI0uMq4dYE4N16S+JtcwtPb5VkGYu/ZgRFR8bG6NwM1t+jCfNv2gEWtOuRATnANwGCDt6ByHQ+pTFMw5Aa+pztKlytkRiNRM2ICQKEVwCkGIIqCHi24VLt6/PSkggci0KKn6bIucjNr0EmYNXei0IGOyaEDHCiv10AwTVRIWKEw1LkIXaCiaUyqqZnqxXadSQghicRon5omZmTBMk6pyCOvtJhCJ2bheK6BazpOmMR8sdNV1IcZU7Hw7GovYJlgiCueFlUMgRUmssgwS2NQmM+uYYMrMQwmbHJSCogiVKMpKo6FACBAUszqcwGx3QJnPQOVZG0YOSW3Oqgh+dGJ1eeK6RoZ7ClR+yl2mHwBXXQq5M2n5WaXHGTXFqc5v109CgI9xuxiHj/zAD5aSbp3byz/wKcvjSertkEMZjSWTdqUojAyCChvMYDtytBKndYouapXLOUjzIll1gu2Drc5ccUSTzQwIYOLEsc9kyoB3QPFo5QD1BPl6d2qN9SRv3qlXwsyKXAMdzFofbDCQIXQsLIRow/r0EPrtr/4Jb+3FF15O3F1sR+Lg4a4AqqVYYYAzee4ssCysBRklMBOKgC5furzebodxMvflRiQu5THSaTslIg6xG8eSchmgVLallBBj3/dMKGMppSjlMWWoHUocsxYHu5mFO2ZSLtlMIVnDOKSShz52sZNABCFVTlbPeM1Fc8kMoyBBWFlKKQRE8m6FsmACQ3OBGu18Ju2KL40cablwC4XzVD21VkqurI3b1Swe9VxK2pEjzq43Pd6+FaKaioPjNo9t57CBegwXAin3XXf39rv//J/9/L/1M3/xzT/6zo/+wIfffv2N5eXFyx/60Nu31xYj+QFkNH8ImaHsTojcZYQ017SEG9oBXM5Rafa6FUtN1sB+SBeBiuWUfBaVkBCX6knnI4Ga120dR/6DYSZNqva0SrW4wSErSmQBmXJm7uODO9+FTXq+Pr87PfuTT928fyflNKVCYA/EZmooCgGMzLwBw4wUpEaqhcSOLx15C6+noEVdOC5DzhKFRIAUYlSzYZhKMbBAc4xBQgSHMedhuyXhQQnch8CaQ3GxGRsFjmzLlSx6FgnTpLa1xVGfppBygWEViQINmYtPPlZTVYbkNKDoYDnGCA4CCIxB0zRMGSEGUi/paLO2Of6iDe6DtWqkP9f56WLfvHYm5pC95b+ub6zvsJswM39ZKyKYVbHPfhFqxwfXTlJ3qWWx6H/4U5987MknD1ahdOGlV145n85unZyU7jgL97meYeCG4pYlc3WHqroRe+3maMbpntVRzH4jrWImpM39nYgANo0jAezn11ZaoE5t8Pf3eh/Vo8Ya5JpXGj4Nz4jRxkEXY1KiwDxxWBIxbbfXrxxOTz49PHp0cXGxXC6nB6cpm0CKWjEzywwDmRIJMZiyaU4FrgogC100wu0HD4aUTV3hYSKsRJqS1mcsPYc0jALrF4FItlMBB5IwZc3ZNmNWM1DgSAClNKacAoej1QFF1TKOo3XSL4MsFqHjMeWhW8TNxIGp55TyplAXuiAlh0Bpku0wCDGxCEnH4BCyYcg5GylLAgBRlmJBYN4sqyAGuB5z2DJ+T6krBNx3QjMr476xGcOe7TGoHkzj0ZKx15/l5lK/rfbhL5hzi5Y9oB4LrRDLaoeXLn/8Bz8zbO346IohXYyh8CpY7kGmhdqgEzQ7hFkdN77bTY5kqim1qc0wILbMqSX3dQ+xx2VPraBQCyJ919O2Al0hBjG0zd4xE5//XQpay3JLoBwx1vt29Ui9ELKgTEYBEthymaaPvvTiFdluDy+XeG0q54er1ROPXCEKZyfnpaixGroY+7OzsxhjUZ1KLtkICFyiCICU7f27J0MpChZNnoiw8JCK5SxCh/1KgozTYJqZ2DuWsqkBaoU4gNmIcrFOsk1lHNUZMmXKKJOVvuuT5UxEXZfzCC2k2cj6yHkaN0NyneGk07ILeUrjMMEQYlQyEo7C5ofFsBh8+jqMiMFSj48RwHycayFqI5OUlARS2HnWGrb2+jWqr1Ofx7rLampG5CORanK0N7hq3+k2tpQAmKqXp5yd8gIVEQFCZCBVUm89K4VIkHJG7IqpUShqsCS12tXg5i7baye3tI9k14XW0w5cQaNAmxXhpJspCD5om3nWCbqyhNKwzarEJIVMGGysXKDBbZ1Qj76rx5XUznhqXrr2fbatWDekIQcAFrjEgKKaC0J3eRVLNxWNXdAyXTtelFSWVw6YKQTr+4Oz803MuHbt0pDL/fPN2XrMqmZ1sOP5poQYQBKI1ExLUfVz3wKMSiH0lLOtt1vfutux9H0MIl0fc85T2hrxsN0SR2UASDmFEIR4ueyjcN7mnrmPHCO2wzqpJgcQokzEQdS4GFB0FQKITQIvxAzKZNBk5UJVjYrCiLU5vWxgplCpQTDQMbyoZ4wxZyU2kWK1C4Oq4czSIXKq0Ex3ZwA4VvV/dQ3UPM5yb0y1tf5B3al3W/ysxtGEP+7NZhhcZ/kYoELZiBUhYFIlhZDU8VMOpq32/dXC/v5YgNnLN1OkNi1fdcY2zqxV0cJuXDARmWYBAkMhSoVVC5NiCiaZVdCKItoIf0L2mQnYgxRWCap5n7dxkwwg9LZhsHHYZowFpIilDNOUIyWFZu8IpRhDHyE03Xj0ar9c6Pn60qJfcphSMeJJc4ExQUDRQMyDKROxSKV4RWA6jAOIFLUK0sVOjVi6UjSlXFKREBddFInDuCWm5WI5pamohhCEmcWSDpH7iykVVYldUiMjzlCbhGi56LoozNSFbpryyZCShExMRqImZBOZEZk4VDFSFfH5ympqkXm5jKs+LkJYlBS7UILcX2/uXmwnKwnG5iexNA0JjOqwDdeiSVPe2o7FqePa4Lk67TnMilWtFgOrBc6UZLODGRXMoNfzCyKyeuSTZJdzsniptBalmvNzZbt3xzSKqbUxudOeh+OhDS5Fo488SZJ5Gqt6RcujCxEj+xhlYwILsRVmouICGqtDhmtLpSPaXfxgomIPh6FdOKqHmASzrErKPYcOlHw6UZHAIQxlSkmH9Xa56E82F2cPzp55+tkN7HxThsIZoNAxStFEFKZxsFIiUwYxiTKLBHcXpeZKGEqREEHEMBGRQASsFhxC9yANYRFBfLBaEIVUJmYJIShUsw7bbfJnVXiznsyMWDpiBco4BcJi2R0u+74PbOXo6AAGXS40jpvz0QWXzMqzTyOYaWQTQcc4XoYQQ5oSw5YRy84YIx/EVFKZtleWcRUOzi7GlLQESqZj0lJ5Uw/fJNWIMFfpgMoaOqNYT/0iw+6VLZzPeXb1ZXuNapi/nZFwQ7rWgK+DOPXJuhBuZVKHnmrSJt2hMbHcyPBqCo2DbHylUZtbjDozwWqDSe25dbJWAQhQYIsuem+HC/MYxow2LcgxWp3ZvdfiMpfozHyEoLaak+tXUIf5hHVC58QvZVCejCYShuRChGBdEFpR7O7eeffrX3v12ec+drG+KJHRHVhQqY/AJBcJvWnmEGplt1ipU4FEDNM4Gqw/WIlIKXkRuQ9Y9HK06HuyrhfTXiHFeDOcb8eBvPcl5xgiB5DpYrFYhBADj+NosOVqISwg0sgdMwlWfQxiIcY+MghDStBxyZYMQCEqwhbBBCqahGnVB1hCTldWl4nkIo0SWS2FrhMRiouTO3c059WSesLRAtpJt4hKuHO6Pdsmla45OrV27Ny+D/DA6omANu/Hs/E167Q6lwFNodHep56Ngub3rPbNzEh2h9a8v8GHHlYiwAlX8nxlZt5rNddxRfXw5FQSilVSzfeDi2XbANdG4jb9bK17MJlqiUGCSM6JeYnWcsQMy4Xb7PZml833ewUYRqiDtDzsVBrZo48CQOhiH9hUetiUxiIcWRglBuK16KJwCJSPu9//l39056tfu/f5H5MrV3nMCj9kmuBZW4fu4MDUGN7zZExkJfcxFlWFTeVQAQqx5NQXXnXcsx72sRMaiqZRU+au68xUSwkSuQsSOwkBRFGC5omZSpkihcUiMnC46InNLCeiEHtoMs3dctEv+tB1OedUhp5xbRG0mJGNORF7XstmCIGtTNAE1Yspj8Om5Lyg/mC1CH0fI222aUqFW39MAA9TyknJ6Ljv2Gg95QkMFiODFqsMVI3drVmCzCtpjVkhhk+LrofNUR3P11ro/fm0jr+ZN2zQgJt5u+7cX0lGWufSVh6qHfjB8A9qTXbu77XJA5o3htF8+huEvcRb/ehcfDJrsm0YqLQJEmxmw7gxna4dHd8frBgZsc9Pbnezz5S579xlRN6+PGMbs535uqcLMQSilBWxj4erBdYbjp7N2SKHKeC4IG3Xv/HLv/D47Vw2G756pfYlFGIjYxJoqSEhL5hWIQRho6LZlguekk7FKJVCnEy7wCFEzZMSZ6XhYpxgqkXAWkpO07VLhxKX61IyDAYmBAIJBxGJCJFRtI9hyqMRsmYrGqIQlZxHog7QadierddJSxcjhIbtIBwU3vOYYuyUVHMhBrGA+ORsk9LUxzCN09HBkmBWbJwmgxVVNUMpOWctaSqiCkMRUIAV0+IqvVns2B4J9ulGzOMLfMwy1+QZdXQK1VhNDZKSD15sMGAHBKil+xUpNgUnM0lgLTWbQSW3pMJZtCyZADwksPL3dLWsah0W8LBRksdqFtaiNQhU8TuDiJhWq+U0Du/eeuvwkeezVcQg7rB37doN30AbwPaAQQSUJpD11WuLCRBCISPV5TK+8/q3xqCrx188ySYRMhQ6WHK2kadrHH7slY+8/VtffXR5cJKTEStIiATCbIYSDQEWSBeCg4gQeMppojwOJWUtSuKsgk0eJnLWlMp6m5gol3HZr/pOchrI8tHhkRaMeYIaK5hotRQzbC/Ok+CipC7GC2BKU84a+0UfmWj0oQjbpOfbM1Xzw985EjGPamQ5q0pgRklpYBYDQS2yGGEYxlKKB5XNdoiRGbZer9frdWDp4iLlvBm25HJ94yGNSX1oBljV3OXs2WjLUQyt2b/Gck84mubfq9bU0vzZOQLUVCJ4CIs6Xvs3pBSmmtkiAXPrCre8pL0DzW/vzM9sptSYB24u33aH3laL9fGOVc+lhZv4gJnUChH3y8MpnWyHIcaFUiYieMCimY1wqbx936V7oOeqHPLoYbLjQhBALJ585eGdd958/vpTZZu+861vfuCTr7z65d9/8QMv0SKOXeyvXT09u7h/dhIfuWSqBcX3Qcqj5pGMClmGpVGnibu+S0mHMYMjIKWULkQ27cRyViKKLNOUXPPL4M121KyLDsw8pSlNRqWsYogsAPK49YPKxqxEQYvTBQIWTbhI+XzIBFbVWydjzqnvusNlDx1LWZOEYZi6GKIwiLKaqgZ4Cx7UrJSctBCgSgYep2kcg1keprQeUyA1GYZx2k5TjD0xOKCAsw/VY2IQ+YmdzC0020Or36zWG+i8R4xrnDWCaT2yzJqrMh/v2ipYD3nivTenGRQKU5oKoMTiveROvLSTsNGk6+Q5uANef5EDxNZbPGcw1gwTnr0YYOrT5uthEO7aCYCpEavxmKbDGOqBUnB6odHurZJURyqj0WxzOaPF+fnuKjQ3C2aYhmFB3U9+/nPL9LFX76y/8/Y76b2b+smX7r732uFbdz7w058du9Vidf3jn/v8wROPXQDBYFpcWeVsblFVQwIDvFaLagCp9CTBnUuyqRcxVdISYzCQCvWhA6y0wqgRF1jJWpSsGIsxWyp5Mw0S+kELczlaLQ6WXQx+0A426+3FaINiytNmO5IhBrasrBqFt2MCF1XLeeqiLImLQk222ylKPj5aEWMzbLSgaCk5x7gi4lJUNV9sxzFZZh1ON6koibgUBAXEwZjUiMBOYDJ09pJoDtDmkiZ27tHRP1X6SJ2E0vonTvXXzEitoCnu/k08TM08GJWEFhHzg7Gal20TIGfAWVEyVTbUHXxVHTC5AK1WjKjK6UAAqRKzgUop1oQt3h/sCEPNOHRPPfX04BUwL+oT+TTGffQ5u3iP+H6hpn4OR9OGtTTf0//QLfWP/+Xvfe6Hf6S7mEovN8epu/fu6fi9/8//5o8Wq025/MQH40+//d79N96+FcPFb/zDX/zM//ivjenm3W9+7dEf/qSW0FkYeyIKsXDINvUqZEFNA/fZ/ChZZS5Jz4ZNhgpLYUQJBktjAqEQwyylskkQJmfCGZqHiUSGaSxmHUqessKmMpysR25nwZsRiKNQZO6FqPbskpluE4zE45kSDUm3aetHXk3TyESbKYvQdtiOSTNMiM7HdDykg2Uk0/W2qAXzPuUQICLSQQT+IdY4GfY5MFKtgAGwS6bZhZ4u8ldjn/vsnRmmxD71jggc3MyVjImEs2aJ0rm6x0zz/JgVtCsOObGa1I57Dl23yVkRjIzYSKvfqzPzzVfK2sO3Fktnw9FmrObAX2sp3s20ckskfoy3V1AjMbH/eHYBoISV5l65LEpiYgi7vLKYzu1GnlU/HBPgDXbSCve2c6kgovDSsvvIX/9LD157/513348ffGooqXT9sx958bUv/ervvvn6P7v1hzee+8T26affffXNP/ehp19+8cUVbYXG47BY2ipx6TbrSRC6Reh6StN2nJa5cIiaMKYcXJbnJ9K6Prpg0kKRmUSpMLMEqcUSWJpSyhOBQJK0KLIPZttOScAE4kxJSynJc4/ixZkQWjyyRd8TqJRslqBWVL0MWxclSAi84kWMgYBhs1nFfhELh0AcxpRy1vWQGehCWPaifuYwqNSIVXNMtQLzFlQn58lFFUJGWggahYtZUTWSrAaibMbEwWf5QVqJ3kM61XINs5bSS9hut7dvvU8sly9fPTo+Lj7sh6Sl97tMiICui49evvTa2/ckVJ0FzdnTnOjYHEYbi7UreIL8/DyDAS7RMkB9wmF7m2Y8XJlRMwaWkbsDufbMM0PRbnywkOMziio9mZJlXyufaLTnQr0c0GhZ/xXNKRRo5/nZzEK3vn949ZmL56/9Z/+nv3/3t7/+H/2Hf/sLZxff+I1//fwTl7uDD3/5/GsfWA7vLafjJR9eufznf/a/s6bNg1sXL3/uc6+epdxtv/fNrz358ke1tzfefO3px55kDpxxutn2FkaUMWfNJbC4PqIjL9BpnnKIQuTHNNWTKBjU97HvoqoShSUBTMVoHAYthcEGC0wxdGrBvUDKZUypwAArRYlpM2zrHRK5XpiZmIiNui5yR0REqouuKzkJ8fHB4aJDgSVlr2w5gxb8nEnnRJhiiLkAqswKsAhndUVcFe5A1cwCLLIdLrrVcpG1XGzGzTQRSTESZhc8lurB2og/q+yoKcEsEEXmd2/dXsVgwCIEqsPrzBuDpf5Ry6/NTHUYRhGB1YERblMFztg3yOnIVkvLkPZSaYrVVP2djchlrHBTr26P2vRAoKDk46PDv/N//j88d+Pqv/uX/7v/+X/xd378c5994rHn+0uPDRlBSKBWqI5nd+vfRfjGLjR+ojZCtV80RA4zC7/wT3/pgz/yI//kl7/whd//kx+6H/P/65+cfuLwlE+E6OjG1cTK24OU1nce3Pn2V74+2ebk5u3Tu7fKYjg+fuzXfvcPfvPv/n//F//x/3r52KU3/+jrL/7sB7/ym1/85I/88Ff+8A8+8vizz37ylffv3jleHY3nm22aMgw5cQwAWym5eiONxgbklOqM3KLEJFQqOjF04h2vDGE1JQIbclYFFUHkcCjCIXgbZU4lhADmycyKCvsIPfIhewUFgJayThMZZLXaqKasOatRKEBpA20KEWmjcEA5ZRAJXA+SUbxFiWFEZiLgEH28+2LZHx100/a8X3ZyGOlCU7ZiZdEtVMuoQNFMULQzB2FsVqeAkif1dnSwGrbr40uXTZVAbD7RxBht5IOT+lSPwV0sFlY2bfZPdgcUfNwvsRq0kltK7VQM23PG7K6Ma1ezUSuPV7NpCFvAfiIPEUBpyj/5k3+2rB/8J/+7//3VG1de/sHPTFs6naYOJkqJmVGoHjDdvDXX2wV2nTMwsLVjnEEGk+ZNmSn83Bd+bflbv/Pdr38jXXoay8NN2KzKweefvXJpxX/wr19/5dlnf/GL/9Xf/PRnnvxr/cWr4e7779x86/S5Dzx2dnF7uKfr0/V3X3v9D3/z1195/N966crhnXdfPXvju3eevHJ2+63b43R/e+8Tn//0f/P//ns/81N/7voj13KkAJq2IxOVeswtmAVFYSax05zNjEV8KJJPBPBRgpoLIaNwLiVE4YrzKMROyIgKWLx623VkICY6mA+iA4gopXT35M5isZIQYteRBa+oTVqyihLX5QrsBLciWrUWM6COzPZs1KzkidmhZ2BohJjpME3n52fQxaKjzfpiiYUqkWrHQmSdTWoaOG6zssGEpEXPVKBcG6RUFUHioo9dtzo8GoaRYIEhMDUlCBExI+c6tXm56B/cf//k7ntHl57YbLOnL3UybE3rWqG1KfrRjpSg6spAVuqnu/zI8UZjXqlR636elW8joTCm/IlPfur+++8u8/lP/bmf+ubdAiyIdVGSIRIH9rOvyJtR5xCvO9/ZrLJtAcfn1ZxLrcVv5YMfevz9WydnQ3eR740/9Cle9j/3c794aXVVNnzAb/7P/sZnn37h7Y/92Ee/80fTL/zc33nh5Z+hcJMiH159+dr1Nz/92U995MWP3X3ju1//ym8d3Hz0i7/5xYvvfvvpl5/X07s3Hn8i5+ljh7zK51184o+/8buXZPXCM8+mIhKiaRZYPf3bE0sKDiRrYxoRM5FBYrDeoKpkQrHOVSFqDAqTH/7sEw9r+aUKZNzbsEhJU56G1fGRKoLCEMyKUSEhUWZqLdpcTwqhxvjUnngQLCilokqKMhYOZigZsJQ7tky03g4lZwXlcmZTuVhPygYxYggQphBI/LxUYrMMg7GagOLi0BW32XGF4eqlS0ZaVK9cWommo2WXMs4nI7U+lFUvOdtUyIxYxxs3rr9/+6alKWsppXSddEQKSuSjuNnPSlTvutfJiLk2SfuBVTYT8n46sSs5GbNepfZle1pWhBNKnxVE59PFsF0fXVpCNFocRZm0IGRCsIFZtC4jbCY2MXNedXSJqnopq2KfduS4P4Jwsr7z1umV771x81q8IU8tx5Xo2ft/4c/8zM3vvNW/ssC98t7Jc7/6d7/z9OWv/8Zr6YtffOdn/uT1H/zUB5958ZX3xm997ze++Kkf/hw//cSwedc0Xl0cH1M4unT585/50Y9/8gdODy5/9Ztf+ct/5afeO1u/baeL83SwuZVvPHEaKEAXvGAWQUmeS7TdCiLSlsSZ+3k2MhImaGBmV2VTBe1zoaIxL9UpuLlyFXNx1y9yKttx6MICTKalao5QTwuu/dpWwyRXkPfQQDwmMbIoAuEukqmNIyUtItaHuFr2xMSqQZgXXS7TlOz0YgRIIneifRQrCDECYNJIHIV6FsIonRhzzsoiZJrzCFSqmaF53GYDEU9pOlodEJlqIZCa9h33EYeraIzDw8PNsJmG9UG/WnQh9PF8k063I3EIVJY9lWlcJ7Aw1akkXh+yDEgtNmpFsEbGqqZEwb2cQQwcqZalqHKoYrApTTA1K9h1ue++GuM5p+cPlTS1HQzUwKcBpTU5E0DhL/7ox0+x/ZGPXfncx1556UPPnR4vv/f1u688/yw/d/jv/JWf/X/+/D/97T984yOf/vSv/dKvLOTypz8UFgfWLVDk3tt//zevfwvfkvsvfxKPXn188aGPL4+71cFquHb0X/+zX7qKfOnHfrLT/I0vf4mPrt5d8ptf+eo62As/+VP333v7X/zqr/x7f/1vvX3nDtHFI1ceGzV7FKLaF+A0RyuLeDspgZThjMDMkexj7T3SF21R3HZVNQg/+cSTjvyL5v1JRtZSSwDE3mqz46eZWdWkyqYL2HKall3oOypZAetjT2zEJEGmaSJmU5Uooe/LugTRpFSKDZazFWSImvlZwF0HJihUIUUhJBJKKVZyF4JYMfCk2GzHGLgwn5xflFKEVCznXCBhuViAKeVx2G6OLx2GYCZ6eNBHkZ5pGSwswzAMhhKJFqxx1VGiMeV6nhsRNDMheuZualBmB60KMhbJJZsPk4CYAapgl9fWZLz6BdMa8EyBOviPUMn8h+rAzZt62kVNAwXsi22UyMkvCh/95JMHCD/9uY+VabsIm3fu3Cun977wa68/ee2R4XSpi+tf/9rvf/E7X47l0l/8zIuf+vwnvvybv/H45Sdeu3Pya7/6W88ev/hnnvyJy4f9aR6efva5eMT/wd/6m7cOFz/3f/wvhny0/eZr77x386v/+LefuvbBj/3VT17//Ke++Ou/9o2vfOuzn/mhJw5kcdy//jvfuv/lX//Lf/Nvk1guiUIXYj+NIykIRn5yF/w+CH4yVuuknEto/+YvMqDU/rfqVo3quE/HAMpeygGl+RCoVnnRYlqKmZWchYMHpZwzM1Q1JZ0ibwKEUTIIJAxvipEgjDBO+cJ0yiknDbEPRKVomTS7bCTllAuAMx4FTFSnwSk0hgDLgSUIM8qUodyRGVsi4fVmKoaS1n0gEbGkalMn/TitT+6fHh0+cnF2BpS46NQ0gxC6CPQB22EcJtqs9XC5ODyIVKxYUdNipKXWMAmsZLBCassQhRE4qGmCSuRiAErKWoiVGKyWlcGAnxDnI8A7AjPVRslZ7loJ97na0A7FnIsHmEN/PcfTHWpVeIV757fHocshKhXOD2LX/8RnX7p3cbJ58+zn/6v/y1SGRw/7B7f57oPvrqePHjz+9PLxR5979kP3vvzN723t9Pi9n31kYZeX06YcXznkA3r06mOsRVeXf+21Nz965XB75fLXzsccyicuXfrGt964f3Ln2tGlo8Pljc/8+D/8pX/y2//o7/97P/4jabg47I/PQjwvF3/0u7/5+U/9eHGG0V2bd4LDZ6gTscDgs2crNiJST3LnUxOqQ6xJAeAUzrxtqwquxvImv23nsYIA83NaiSlEYikle9JrYG9tSBnjlJghzCEymE2pzQnQrABEJHQdAVZyEpBCikkGplwUgYim7E1CEGQjFLWRM0GFibSELm6T5VJgBk1CxBKK2eTbsyjIUik5TcsOYbHYjHm9Hfpll6cMpZKGszEReJryMKYpWwZPyKUMABuBFUwAS8m5pOTNTqWUrk78KkaiWgQlSuiEY1xM4zipkFkq5jobIspFY9cFCYChcnMwmBPDPg3H/2u2R23t5V7NRm3O4drDLTALvS30UHRMMUYNYsBmM1zpj2587Aqpekn1J37sw7cfnNy5ffPv/d/+kxtHx6/3i3Q8PvPnXtA3z7774OSJIR0dLC/ef/+5Z54vhx3dOr9+49q9W+9x/OQVXDo9OyuXxi6lS8vlp3/qhx65ejhN6/Ei/atf/IXu7N4//pNvfOLw6N7bZ3/pb/xNvPX2M+/fOuiWa0lluwVCBBcrCEyqolCmBCUDC5HBLBOoFsoIdXxrZRUJQDsCmJw99JJGZZ19WAIMikBkpu5RAYIyC4MJfsSOGksAIAhGKmqksKI5CQlxoKQ2jsVMVbXkAh/CQUxApABTLYVZIOhYOqAT9vwUkUwzoXQSi2EsZSxFJARhIfnWN/7k8Seeke4ouezfVFS9EWXKCoGQpJQ3a716OSZN987PU6Gzce1ToSJ3220m4aIoBglcso7j5s6oMYTAJYbAINWp5DzB8mQA55SnGC/GpFYOu45hMXJOpQNP07pkZSkLNc5lq1Sk6yGCUgA/fIrJJ40yUCpX656idbQCVDyx2M0QgM1Vrma53E5dAywUJDLzA4yjxJSzqm5LOjvfSqCu6zAlKnj86MrlRX9RBsn5jZuv4nD50595Zfzo5ub2a1/759/90CuffeXJHzBczlOJUj78gcefe+JZKmW1WFoh4dBxWBo9+8FnlXR1eHg/r7/07e988Nqjr37nzd/9vS9dfuzGn/npn5R/+is/+I23Nz9z58333nn+uZe168s0GUnRTACjaBuZUOp5gw5Dycwbz+ssNa2VE7MaJqiFESVIRUfmAnvv3cy55L7r/WTfioZrtglmKnWgXV1LDkIsIXawUrQcdHy+PV2vN9cfe3yacil1CFkpmnL20S+kriTWntSnv3IQGKIsYDoplTETlU4o5zyMWXN+cP/ek088tYiy5MhkZkVIXKWZciIjhrJw6Be9KPdRum5MkOj9lCYUuq6f0jSOU7+IarrqYjGdSo5RoKY6qYGZYi/rTUnZiKyYjdtBRJixwURaOu2YbMhKZiWrSGKylGEQs4k42LjlnIRJS4ZEoOb7aObZvqrnDE56zr+Yn9PuZURkzMhGIAQSS2kklVJURM3UrByKWB8GHcdpy0E2m00Om2TxPOfIwovuMC6PC+hoCU10enry+pdPj7u3390+/rGPn1xsn3/uw1dvPJKIKfZytFheP7C4SdO9Mi31mP7ZL/+34fjw2hNP/cZv/auD1aWDxWL9zs1X3/3eU7QOf/zV937lX74Whw88evR7v/e161cfe/IDz3erI6FAqkCKQQghe7e+qddlmMFkw+bCDKuDA4Xsysp7eRDDoBmYT4JBVmWms/t319vtjSeezKU2bNRJ2PAjftsIGaB4omrss368HN8v4/1b75ycby5fvjwMCfCDu2CGUkxE/FBrx8Ej4EvsCZqYi05RtIBIDbmUKDGlZNAQOKexZO0XEkPwZ08EguVcmBBjCCHksh2HaRXnOEm+2UgImVg4RgaEiFNKVEKodXViok5ETRNnXgURUYtpmgCoek9ZNEPKRjkzU4ySClz7no3BgeOiUAgxpjQFWSQrFU1BfahNKxrtxlQYAWSt7WQuZcHaoUy7Gj1ghjCkrViwbAKybEYmgTKIDCtZxIxxmg4PlkOZHpzfgVIJi1NFvrh/HBc3Di5NpscHx1a2f/y7/3gp4U/e/KJi9aMvfPrO6fTolcdD3yvRcnVIGVf6Vez620nfuHnv5UuXH3viCaP84Reem7qCe+NzH33FvvkNunJw+713/vjijdN7b9lQ+g9++NFHrj04uWeGZQiG6WLcWAlP3ngmZTUis0LMgAl0uz5vHTqBhE2ztV5Jl7ermaBOvjM/ZRWIIjYNx4ueUmar1qPqI1Sqzj0XNJWkOjpiVTIIoZNwZbV6X3jZxWWM4lmClmJlNK+cwOrMOvcbEpi1wOf/C1mgYCoGzSUBRtyFGE9Ocr9chr4PvLBoKQ3TtA0xLvtFKTl0wXJR06QlXWwOFlYonG9TUha1KCglF7XTzbk7qLFYDIFQUsp+yosIZ1NoBlOapi6EEJjYck6LVTdNmcBJS4hdjN00TlA1lABl6aZcFNbFmNRUUymJ26RHFkYpjVNG8wP7X7Z/bkjL6lsuUA3ai1VgYwCBUsmahYOihLAg0JjSWIgAaAqgnHjMiThe5eUkmyEkMprW43maztJF6HEp9Cu5/MiVJ/OwiZs7i379ve9+4X6k20ePdYsbjxz1/WJx5+37Z+dHstJtefd5LguU1773Osvy2iPXhgN5/8GrvWI8uPruInz6wx99Cs/8N7/1K3/jP/yPf/k3vvDM+b3f+eJvH1xevPLBj/AqvvqlL28He/6v/0/ygyGwaPAZh+LC6X7RxRhK7bb1eSqN6/TenSpeI9fnok4NFzPEGEkrr6paZorLK/v18OaSAxEMJRDUOgUsb6dpTBo4aEpaSuwjE5fsPLNabSQHfPqLcXFpsLCRiXDHosYpJ5FYMwagj5LTtFwshDs16w9iStMwpO2wDUGYiaVqlJSzT3qarEylICkThRDaiB1XoJYx1Q1ZNAlUSuV9MzIRRUIqpaimrKxZswVhxQhQnoowc2CYxRiITGCqnGybC0QPdBpoUYREizehFbNgMLMClZlToj2jNKCdfeanzntpgNrw03ocjnNPgbTn1lSz1anrupzzNI4hBmIrRFlJZGmZc9a+W/ZZx5wPD1ejjoqJDPdOHuRVtrAddCDJIkdjJps2J+mboD/+4R86XC3fvHuH+tXV0N1YsMXEOZ3deGLx1OFn793Z3Hji+bdOvyxFf6eftp968r/8hb+3fe/2Yy8993f+r/+Pgz7deeV5HYdHF6uLfH+Vrvz0pz+3OLh0MmxCWMAINM1HEc6TiByqg8SoDSLysYVaJbqVAfSNq1agXeyVzE/prYqwHUJqamIzi1GLBdBIyoEs6aAlT2m9HfvVSgk5JwdUqnWUu8thyVvAXBVg5pJ/g00pZ1JmlJJRm7ONmYsWUoOWVEZjhhUJvFotSsowzTnHGLy+CpYohZlk0enoWaMWmJqKn1JMVGdJ++xzhibNYJgFZlOTwKWkYshkSU2nUUiioYtdVphCtTi0kIwulpQ0l1BkyoU0M5QCc/WKpcCUTIUoqwFl7iC1Nq7fzE+CbzS2J/G75a50H4HdUIOFvFwcDxu1Qlps3OQQogaQGUOFhTsKEpljGiXr1EURKqZ2HDvDmKgcHh6KqY5b8DDpqFMWW4YchiTEHTo7u3hz0LdjFwd9qRw89ewLL8iVq0899uxqtXnxh577nfP7qhDE02zpkWubs2n73tnzzz/2//v5X/xbf/2vvf3G9x482H7kL3z6zZMHjxxdWxz23XJ5zOF8nEQiF79LhVnJ2YJ4LDYQt8EvZrP4wWjX+AIzl7PrlFPXL9S0yW/VfWzD7LA6U9El2kRmgaBFUyrSdWpIVg66yCFwDI65JMjsgM1sbv1FHeDNYJ8bRY6JrXU/+mcp9Pj4iAgMNpoHIqPrIsFy0ZwLEYUgIfZpOCtaYuAenRAZ6owZsjoPP8boWqKaXxoIrEULUEphLQYujm1QyGCkCuTswi/zMysAJdUJbBbUOCcz4lKyaYkhaClQlFSCz46teFn9gGRXNKgVAGb15Co/626eXk3qcNUIELSKnlGIywultDhcbtfKGqIsDCQK1WRmRtrFSKyqIy9ysAzWxWKRJmKSnOMqqJDloUCtD/0ipJTLNI3GxiAty3EbchnzeBFXdHL/D9bdH9yK16/cfPHRS4eb8/XnfuwH/8V//Q/7wwUtu3I+rAb6sR/93N/511/6/FZ+6sf/woml+O7JN/7k6//p//3W3/r3//3x9p0NXy5m4+YsXn4kmXVGRg7oOadJu1jJU3j5o00PqFCn9vQSNQTv0xtSNqvidlNzQrSewQzHnCi5MFNH6NmoZKfxUQrUwDHljJlX5aY7qc5XgTZrGc0xe+Ova2JgYiYciKCqRY1gwrS+OO9iyAVgn8VIPj22mMXAfRemlNIwmKpo4SrZMsDYlIJrEhC7TvfOm6u6UJCqT6NpxWFwqIpMg9YxJITiQpNoot7DzqZQ4o6JSVmLadEypUW3UM0pZQ1kbKrZFEaF/Agybk0ndVm0OoxGvwBgY5t7+c1cJWAgM4RIh6Uk46FfgYmYbdimPi5yEQ6ZRY2KwYxVjKmoaUnFJKyI1SyNOI9CcXlcMquVQMw8iARIP9gpx8TUX2wwbPsH5+mRrtOlnpZ7eHCnHB5ef/aRN+9++40//torLzwXDrr3tveObfPRa4dPP37tD1+/yTEuOktH/USU1xffeP2rz+TjZ198Oi4W3/nOdy9ffkSR24EQCkJKI2w1iywJbT7CHvrxCUR4CLyb5kkMpAY/PRUGVYZ4Sd8fbRAh2Pu37z557bpNmkKJIovV6kKVYCVnAZNWXqWGLLImDZgz2H02ZYc3uKp8/CymKoI3HwpsMDUjNTPmCJjfhBZloeVqYUaaNeeEkogaW+7dJIpSslWNQ12LTsASs48WRxsK2fRNAFmg/eEkbAwmLurD6gomowJQF0IhLBdLJrm4WMcQYgB1JKSaUZggwurhaxYtVV6ea6V193y8499auAOooTOE7QDioJokINAUAzMPQOlDjxAzb0Y77wMvuSvpeCIh3VjamBp3CysYQYUxpUFKFCOoFk0ALE8L8tPLtseLRZAuDoCVsmEd+9MIpG3Ob/zuN9/83GftI9eePLl4/967t288cf2Nt7+zsviJDz3y9lm6GMt0WD708VeOOZ+cbU82947uv3TQXcqdJWxDWhZKmYxK0GIohU1hBT6M1NQK+ZnSxKhinBro3Yy4aDGzYSpGrKV4IxvD4ZaSH4+MlIvdvn9x+frVX//dr3z845/+6AcuHcmwoW6TJiXChJKmLDySwlQIyUAkrKRs5vNovfanwDzrxYfleQCsoisvNBgRUMrx5eNCNpkFcX0XshUXyjsJ7lg2hi52UdNm3G6Tog8xSj26wC0gUD1OuJjBkAobJt+oRGRqzORd46VkL+EQgf3ceT8504qZQlVVjclsgm2NAhBSupCoqaTNdqu88jlgBDBU/axIMzIE9+2tbmR1AEUVMJWiQCEwtQPD5gPK1RDu6nlgFnZx+7RNQ7cULGyaJi4SA0XpVAcQpL+AslIHQZLzsVyIXKexbIf7XUdFzA94MDoygIOWDMtjXISMKcb8yLI7P1MmuxiH860uF3G16qY8XX302kV57fe+/Hdeeg6f/einv3H/VlmUa48s3wdZ1nB+ce3xF5764A3b3nnt7PR/+R/9b588euyv/A//+5e5V+kCScbAYmqJWCUAVHLJLMFKZgmlFCL26Ydeb1M1KDwR8ZA7bIYppW6hLKKmRVU45M0YOx5LIQmrg6PhvTv/4pd/9bHLV994/ZvPXH729v13Tm15+fDw6PCID6+S8ZHmqINmTaEzURm2kICiLkopRkQB7RxO253jCFAVDZgZw2AqZuO4nS7WvYRcNOfcCTrjotXyFJVu8GCw2WyX/cIWq82YrZRxTBy6ECOx1ZKMmbiM0EAIMA0yu1SgSkPRjqn1TigjYvPRiFa1p8xUXLbqvlUROhnHzcG1w9j3emGlFNJMxmZWrGixekCDZ0redUSq9bRSUkUpRVWDa2YUqgBpYWbyiIFwfzhf9gsalGHLRQyCbRkt2MHioJv6MsmUlSWY5NUqdQs63awnZFLNUznszgJLzt0oY+ZN6AMp6UhBpItOcC/GlBS2XAVQKQvaTunq0XJImrKtz6ec0mZ8cK1bDduLG4+Wd+79/Ol9fuaZJ948udOt+TjKtWeeOD+X+L3zvD1B0cuxhPHBuD69e+vtgYw3w43rT20yCXQabLvOxBxCTwTuRdViCGqqRcmnJrKX3qXUQ2eIgJLGTpgI6+2mi1FEcp4O+y7b9qKUO3e303TnpeeevPvu+z/6mU9/8/Xvfe2Pv/T8U4998Yu/1TH/j/7qv3t6cnHn7PzHLh8utuk9Dtloeb6NR/E8W2RBMTUD02QJxOIHgDBXSwXQMK9LgVQ1ac4le2fVmDOJkFoqhamjXKzOLEQ9PllQLcas6zorxXIqZsM4dsIUAjE5B+ufy1A/5aimJm3+jGk9sV0bWC42M5bWhPVkVdtfbS7nXIp2XW/FUIoRFJmKGO9lAapKWrx00TRrgDtOEFEIgX0XCWn0Gh8MKKowhEuLhSVbyLIMyTKwFBNeb85zyEdyEGTZL46naWtM63Ux6LLXUCjlfrGIkz5YdJcP6eraTg1b05GFjUvKZoOwRaYgErlQGZSgK5HFqkuqeRxjtyidDCOVsazV+oPIMZ+nW4/fePTG5aPTKV9Q0bMH9NYGBy++18lbb73+3DNX/ud/6WdeO7n39v3vvfM7b+kSrPJdi0kKFRwdHL35pi4PjlSk61YdL65ff4RZRGS5PIixN7Ozi/VqtZymFELUmi6UnDcihVBU8/Hx5fu3bo7r9fLRZ5arg3ffufsP/sEv/+RPfYZfxk/89I/fvvPg7Xfe3d4/feTycYjLB3dufffVb0tYvfH2699864NXaUkfeISnzZHg9ulJOLi6Xl8ECawWgxwSch4yBWYW5x1UXWYaAhucXdfYdRyiEm91ytCjgxWMTBPHOKTcd0sySyUxc0oJZoRa9DLVXDSIsIiDkzyNKSUKfhZyk7VCXSdbAaGDhnZkFMzn985n0MO9KDOpy3W0gnTHBgQKIVT75kBcBOSZkbfUOk2lPiSCqELfCsgrzFCtGvo6cKNVl1ypFw46DV3kjHh8tE0Dd5w5X19c74JdbB7wkrBcnN48OYp80C/y2PPYsaVFN1I3WhIooENUCrTUZNtNWiwu5ZIxKgU/2VuNMiuiBFYishCEDxZjyZMZoh50hyfD+WZtPS8lLilrCA+OGVcePdzcWGzuTRvdHN9b/w+efLk/Co/3iztXp5sP0pPheJ1Pt1PJhok392+d9M88n4rev/dgM6VxGrtoR0dHHkEW/XLRH1x75Pqrr33n4PBws16H0Kmh67uzk9OVdO++/9Yjjz05pXx+cnbUxbLd3hm/9LFPfegb33yrpMXJ/c3v/uvfe+t770zr9OZbb3/uBz/yza989eY7dz/1iQ/fOF6cWnrl2Rf+wT/6zR/46Z/480/1Maw18ld//ouXHn3k3v0HTz/7zAvPPDXdfLC5e+fG40+eX38CZmkcTe3g4KCUIsL3z+6xhONLl883w81bd7pucefuCYg0pa9/+WuXQnflYHGmafXkk19/9c2uW3XdIjKuXlqsOhm3WyqaYBlZEYbtsOx7jtHUFsyp5JTzdhi6GJd9j9YU7yra2tdhxchYQg3sgJWiUBYxNattDS5rqHxI03S6+DB1sYOxavb8DTAtZlzq2F4GPH/0Lmxtc1L3pKKlUmzFuT4YkbFxNlg46pYwVqKiE0s2nZbLQBvbrDnjYH1xEexUloBeWW9hljpGH5moS9uxNy42DpkD9yhCsA6kOnJUMZk2Y+CeVfqFTHmrVkZARJBBJpF0uaRJxjRsj5axmFkqaUxjDA53cjiHjkdPLh/ls3xM7479R17+6OLJJ2/culniLR2mz3zoh8c0Khc1vTjL6/WwPFgM02CAMInZ6cnZ1SuXH3/82jBeQPnw6PiZKx/N2edMJM0TzM6P+/MNP/XMo1qG7UZHOlouDojzo3m5ff/Banv/v/f5G8fHF3ncUF8Orh9+/NkPHa4Of++Nk+efuPRn/+yPv/32G6+9c/PWve3xJTl9+7u/vb752LXF+cnNm8P9zX3+2BPPvXb31uGjR88turWKdeHWvdePjy8dH17CZGl7ZoE2JX/lm9+6vAqf/MTHGXT7wbrn7fn77x4sRVh+4Ve/8EPPvvRv//hP/PNf++c/8VM/ffNPvvmd771LcrjZrn/2Z//CRz504/aD98QKMW7fv3jiyQ/90j/5jZOz0fruymr5yJOXP/9jP/TMiJMrHW1SOT/JB4dW4unm24JLkRcWpmnqVofRyM5O6Ri418uSDg80oetJ1VeKKj1EpK3/0liMlIohFSSOot5BZMhexmUoLBRrlXgSEEqdncs7FWQdq6aV0KjMnsFZKGJQsK3FKDlbyWnVBxOI6dRZoEgj5U1ikqx5gzGgE6bRhjQaQ48PDlIaxo2yrVCIJcB00fXZxillC5wjJgymJiN3HJZ8kLRY4S4EhrFO08W2WxwuDweFJcWQModuygaj2FEQWsUVlVKmk8XRpS2dv/rWN0YeR6TTi3fHYfqTV8dtUoYe9/12qymVS+kw5UGzsoSx4Patu2+/e+v2/esS0PWL/PbtRYDEBQkjKnckMY46jefj6cWDEKfVpcPVpTBsHwzTGeFwEVnTe1rKU09+ZNUfXfrMS8OQ+7i4dHztje9+6/x8+xtf+MJbb7+3zRta9DeufmBzcfP337hdyhiYF4cdLc7/6NVvfO3tt85tuv7Rjy2ffLS/dnz/S68ePdsdXr4KLlnoO69/76WPf+RHf+iVYHm57F995+1b9+5ev37jzGQZynfe+86nPvyJw+Vjv3ayvhcPrdBnPvH8y5fXB8u05Uurg7Nu9UI53455+8wLLy7D4en44AM/+NSP3Xjm/S995fLJcL45sTi9+fq7Zzkt+9XJ6d0nXngmULo6vXkQr6V+M9wPEYujRz40vYcHb761OiDBeZHVGfTw5U9YSn5ORk00fdQiGbj4MTI5oxMmU+ICzj6xQkyIiJGZSzu7w2b6V6Se5tF42Zl123Fx2s6kdNlEGEZOyVtJ4mY7dQdBsw5mNpWY4jIf2trGdG5RLWo2xEAsQsbbYQqIh0tkjH5aVKB+GGEpXlocKOeNbrY6SBDK4CwpqRyEojamHIWKWsdLHaZSKC6iWen8+DpKWQtZnLZp2cdFv1AusM3hpYjp4s13/siiXrrWW+F7996582BzerEWDu+9e+eFD76g9yctk4SoSRkSZDFM+cHrd6c0TikdHhxbSSLRXL9BJRU7vxg+9OzHvv0HX1sdyno9wLplv4isXQzDOK6WB2+/+96r330jcAzS8YIuP3L5cHGMRXjk6EYp9OQHnh3Hbd/3y9WV84vh2uECWphjobxY4JFHbxxff/T0/MF333jjIPKhnksY7p++O5QTBndh+eD87De/+Ht5Wl49Ooz9vZTL89cu5elCxnVn8e3X3gpPPnZ1sVqNN9fh9Gvf/sOv/9GdaVjlSfv+5Ic//daTNx5VS13oAk2np7dOT9by3unmvbPrkKMY7WJ9dv/k7r3zb/7ht5T56Q8//RT6icMjyyz23sbSH/3B9uOffPH1269u5bGT4wWHxZlFPT096qZrcTjZTn2MatqvOiIqdbQdMwIBZhwj95G6uGQTGAXp2J914ZILYEYFbSQF5pKmzQc1zWqmiiGoYVO3X3fWQXCoWbOWlCdwWG8nCnp/LEumJ44ua84l2+WwmKY1aZAQdMqJjCSMqYyZ+6VqHBE6EtmOF8QhBmGIqF1ZXD4oU07ZGF3sci7jkEiYhceUyVCsROmKRt0YUDpWqPaxn2yacupinDaTZZAIBRAl7gYEy6YljQtZPfXUtUduXH2wvshGVx47mvL04P4tIysjsQUMGegPDy5drE+OjleY9Gx7G4suD+XgYCXkvc4LKIN1cdBJzz3C5lzTxElz6gu4O7sYn3zyxocfe/TgoD88WPQHC+bQhRCkE47Esh3TWEw5CIJIWCwEICtSNK4To8iYRuJycLDIeZyG9PTx45nSMG1Pz04FA3fHr33zOxfnJ8tetuuTj3/khetXDpXylf72q9+ZTibctm8P96fDIlefu/TRK+PPfO7aaLYeI9HxUvh733nrymM3punifNzeunPz9hvvPXf92aefuHHz4v5dO9tM4/2vffmFD7y8WHw0rYdHnn3s/de/8dU3/+SnXl49en35q7/5zS999fbHf+a53/vCPzm99CN3l88mY3782svXL3843+5u3puKyqVDAr797W/fvHnzwYMHi0V/eHh8fHwsxIvlkbGUcXP3wa0uPr+5d/r6Wyf9tcWqpwUf94sjP7ygNuuYaWtBbsfE21y4aMq7OpAMtYShTh+HDn3RLLIc83a1OkhlLKpbGzsOhDiVZModwpV+qUIXw5aAbhHTmCIvYrcYhyENpKyTrfs+Io89x1yMOVpCxCKwQiC92Dh1OSpMU2GKzGDjkkvGJERUWIlC6MpYosSuS10XkyzXw5BMp5y2ZVgdLQtMFT33J+fn6e79sOgXQZaGy48dG+jJ64fbcTg9W5dC52ebYVu24wWRDeOaYIHKtN7EEDbnD7bjhlj6/uje2cX7t27dP73Zr7jvF91qRTZEgU7DIsrHf+Dl5z7w6MFBPjyeuljE1l2klPPhQRyGxJ1o4SA2bBJxnFI29GMidL1mHC90yjat5eRuef1b08mdKyXh+nOLq49dvXb12uXD1aKTxXL5Iz/6YU1hsxmGYV3Gi+3FyenZOfrLV/pb6/WDxye68dgVuba4c+/O+u7B4bMHXVwfLAbuwiT69Tff2fzB8AM/9MnV6saHP/jiy88/d/DYI9nKg9/7+oZPDp+5PN17/713z0voHrtyeXt+mtZ3r0q+dfvSe+9Mr31DXvjIByksb8hjB+X8IN+Jy6OcTldnJ5Od/Ys/+K7lFXNQK+cXF9vtdprGxWI5lin0IY2JOORssPKBZx67dPn413/3137l9/+wuxK7yItw+cWXXvngB1/uaJlKIkLX90F6H2NkOnndmXakVROfax1EQKBAkg1FC/3n/6u/6nqCGDvASi4GRI5TKkV1GCcYmK0LsVvEKSWz0nfu8xGCmNlmvfYDjIlNRJx0DhxiF6ZxksBEiDFOKbnMn4lTTmbIOU1p4hiEqKQMEBiF8kQl5DEKhRiMrJAWtgzdlrTNKWkpUAmy6npLGqUrOROYmRfLPufULaSXwFmGlLbjWFRLnkpJZSp5K6GXjJz9uI8EK1hPmRlaUgikpkXNIB13Lz/39GOXD87vv/PZz7/w6OOXf/tffZvBz3zg+ssfevGLX/ztD7787LjJx5eOp+HixuM3NutxdWBMsjykcRjJjjWL8qkp27S8f/v2b/3SrQ9+6LFnXrlcSjBEtUVSyaXOMWbuhBdEUVi8SySX8fze3bvv3f7WH31jczHQYrUuTBKY6+m1zNT1seQk3WKY9MqlSzFQKdPp2Um/WsTAkWkYBk+0j46OzHWxkrv++Ow8rc+3fR8ee/TydnuSzFIuWTVQjBRosov79+kgWqAxJ+m7okhT0WJjQVIrWspUQkHO5fj6I3/7P/if/uo//oXf/epXVSJRYOYxp0tXrl46PlZNuZSj40uXrz569foTIS6X4YBFmJmJtJgEBlB0VNUYo6kROKecUuoXKxYO46QiwoVLySBo0VxyYJ2mKdfKNZVUxmGQcRFCV4qN40SElDKzxBjNwrQtBgoSzczrYaABgJbS9b2pGgYiHsdUtMQYCVS0EIhYxs0kJEx+fBGUUJhTWl3kUkomJpagZNmyacfUI42AFtgUEDhOHRUJMYhSPhvORBRG4C6WleYUFJ1Id3Tcd6KaGTqmiXi53gyuFtNsR2k0Ysgq5UyGoNAxvfSxV9Kk5xd3P/6pJx5/+pFf/eUvPf74i++8/+7y8Km/+3d/cRrp3fcKo7Pw7vaMLh/du/Gk/cSf+YTwtX/6j//bV37g5TzdjAvAlosDXD06fO6xS8//yDPIGB8Ug26HreoW6FSDqZClXEpWCV3PLH0QZgrSdS8/FlZPf/anXvq7/+k/uHnztCyIQYKgZiyBWKax4xBt2ESRBzdvExBiiKHXteWADCsap3FkXpw/SOM0xRAYXPQ+iFkxjdPbp7dMNeUxmRkklzUZcSkBoFGHPBZNykjZhAOTqOWCkkuJITJE09QJhMoq0FLAvZ91bUK2Pb09rm+xkQhvT269871vxbjMykRd7Puu70OMRKKgGzduXLt66fjo6Dvfeu3s9IKFh3HMOS8PD5erVRDuYVCVcRwJ8AJg4QSwQYuSCIOUJIqwWQ6RU5pgEKGcJ4P2fVcUWsqoxdSYZCzZoCKSc9lOGwCqJiJQlGLDdogxeg2FQFP24Qicphz74HUXMjZlo6BqORcGWwEoKtDxATOKZkoGgo4ZVgpzjAeae2VMAo18kYvwIsQw5mmLwpyUVDqK3TJ2YdWvuj6O42hmoAU4JsV2O5JaGS4+/xOfef2dO9v1xYc/9Mizz195cHK/FF2s5NKVEPnspZeeunL5+PTBdOPxa9/8+jdf/NgLf/yHr/3MX/hBpfIPf+6ffeMrbxI/9r1Xv/GRT7zy1ne/+8LLl7/z6r/+wHMfuP74Mm37D3/k8dOzB89+4HHqOG+2pZgVE4q5BGXpFgcodnbvgVi82P7/qfrzYMuy7LwPW8Pe+5xzpze/l3NWVlZlzVVd1fMAdKO7AQkjCUimKJFUiGGTDIqiFaLosBmWbdEyZYcZDioQpE0ySFCgwiIYBAhAAEEQDTS6gUZ3o7vmubKGnKeXb7zDGfbeay3/cW426forI6Mq60a+fffwre/7fXtrm2dqV288sv4j/+EX/9nP/1alrsuSTclxTB0gd10ORZFz7whB7z3kTETOecTkll5LNANmVwZSM0X2waslAEN0poxIwUJv1shZVdX6cGYE4iJi18UYEC0DGKI6NGTltJCo2qQkJ0PlRu1xc7R7FAZDSQYG2QSYxICRTJSICbnWZKCIc+kxej4AcYr56lvESt6HxWKeTZ1zfTFgNlUD53zRtm3T1Wbm2BlQz3XWHvNPKD2ZIzAAOOfEkqExsYgis6nlLLhkaBEyiuTgkbhYBtkMVKS3TLvCi4gBmGqPJFYRT2y6tHwtoxUpZ11KEj0Bx1QdMXLPTDWVvvPNkBCVGRwq59bMvJoKaOR+vC1oCoiiJKDsvWj/kaJ3KNoiWTZBZo/mIKC6g+Pdpz/22J37ByDNE4+d8Kwfvrsgp089+ygHOFGtHc6nTzzzUFPbQxf9alE988ynU/BPfHL7xNYW6NFnfujsJz939r03750/f3JlFU6fs/lxTPNqdtD+0e9fPXW6fOf1q8fT/U9++hPXr7/7I1/9oa2t4AZecyJD5wtCZj8eDdfmdct1df3dfOKRsynce/ShzfWNyZ1preacsQotfRYIkpOKGKKCtTk6pn7c7Zw3BSLMIs6xY0dESASM1okj8s4BiqgAITOYmYkyoHMkYGrGAUpXpFnDHrh3oIgZYP8+7+rMyOjdaOxT6sBoPF4h54XUTImxkygW0QiJEDXl1A+OyIZ9KVEfDPHoJalY0pRL51TFVBlZYzQTh+jqpkaElCIzxxRDCN4XdVP3DlNkAjMCS8kAULU3XLAA5WyIZGLYh1HASe7ny2QGOXc9uIWYkJ0nlpyTJDAgIumNPTn3s4U+KcREan1pIgEgEauppMzOLYGcov3cmRFUjZ1TBbUe/p9hOZ0W59DMRBSB9UHHCYJpyqrwALTuEJyIEBFg12WI2pHC6bXNR87vvPTm+7l1t69dbecpNSUic8gpS1M3+7u7p04/+u2vfWM8DLwK2+VkskLl9nhrc3Lm3OTipYerDf/spx49uXMitdPi05e+/4dvTybl0UIqVPagwqd2Ll65e//wtn339186bvPpnfWv/syziQxRkd39K7df+cV/Pbtyf/b2R4dPPftf/pN/2C3m65v6yFPbt777VpfgcJFX11bYhdjFf+9LP/b+u+/duHnbhZLRwLKmDICF99In6pNZtk6TAhAxOwoFOHSGnDF7H3q/ZgJyzGQgKSFilpxVi7LoNCkSuWBm6Bx77D2gYFZWngSTNLEDdL5OzaJtyiGRQzAQgBAGqaWYUhmCAVRlEVM2U7GMpH1zRD8zdYEBOMXc29DoQRtoD3Z1ZuacK8si54RIKeactH9nBefN1IUgknr8C5hKzoCYsnrvi6KYHk8BwDEagQhov0GaOkQ1EBMTQSTDDGbUrxsEUUUSNUUit4wJW5TMzIgsiktIPxAzqMhkMp5OZzGLc0wISYWJc+9L4iVYocf5gIAqsvO9y+yBhcigj7UgJBEkFMmi4p0XzSiSgQUzWfOZH/rK97//7dt3UIERXOCiqMAx9/itYaiC+qt37q77laG6q3cOTz+8Nq5O379x7+D67Pd/69bh4bc3dspseGJ9c+vceOPc5KmLGxefObcK9MmndhaGv/fbfzxaC7evN2Hkn/30Q//gv//tj/3Vn8TCQ4rOTCC8//d+K7y0e26ycXvy8AftXpsW7B8+2H9zfP7M2ke3R66Yp9aYBXBgg9evvLi2vbqqxfHxkQGsrKzMZgsD1+SYrKsKHvjB4mjhOZAPScUQc7Rs2SwycZ4tvPMIAMjMhAjsyCFbBjI2yQZKZiLmmLumNVPvnHekCMhQoMNQsZckbVkOiFwXM5gSQFZisoKD9wUhiojkzABmvXxDSbIqKIBjZ2YgqSBWFTVlcipL0QkRnWqu6xiCNyNE7HnHAEKOenefiKgiU19mKS4EAyMDM6ibphpUYLpUuciQUCX3ZuGyrEIIMbYAoCAIREYPbp+mKojUG9hzzqLCCIwkOauoaMqSezODmYpm55bhMyJm669KSkiaVE2889pHcHoPp2YzISIi7iN1IgqghGr9Vq1mCiBKAMpVYJxPF5/53KN1fXTjQxuvDZJ1kntYCJpBzpmYGwBfhIMPPjxRTmZdrJAGJ9foTHVma3MwrvTNGxDgS1/57Le+9Z1pbbdevnP2QN9/8V7+5ddHI3zozPr5Ry988pOPn3p4o96/vn7KyK8+9PzOC597bC67Qw0WqqMbB28cT48e25gnlRNnD955+fb7L5299CPXroy6gxN+L3YFKognIxUBiby4e3RAHtfWnRogt36QFSU2ncZUTdba+jj6rgimmOu6LspB6UfzxcI513ZNUZVtzkREpjGrmfrMIL0hkzTnoir6LrYoxuxNRRUkRufAlBI7DgEhW14UTk5srhr71HUiKSYRw56mqwhG0HWJmInBEIioCEWK0RTUBA3EiByDGfcYLkJVABVA7EsOHmiofSxU+1JU651UqmoqoqKIzK43A3apBcSyLGGZ8UZE6hkIPgQVUZGYIiE658uyYEeSc2/JXVI2EUPwugxtSTZhR4xERCDaZenabj6fp5SIithF731GQSBi7DMJIj1IGR2xLXGNJCL9ntq7ZnLuK6B7h3LPG1pWw5VFyDkhOUFnsnjo9Ppjj1z89d/4ncHohOTaVAmdmKgZErD3JoqMZ+d28ezjV5r5q8cHiNXGeNzMk0EpM5kedSnBbN6srq5Pp3E0GgUsTzx+Sbt0dLh/5XZ9/961l383pI2Xf/aJh5868+wbN+Y/+tOfgpxcLJrCEZZhfcP59dHdtirL9aIePnppfv2geGo02So//cnVe5c33r7eBApRBHtDHJgyohoDoEP2WDD5ksfDagazZt4WoQgrBbNb1G1RhnoxxUlUly34pN1w4rSLXYylL5u6KQuP3rdNqwJN04IU2pEjn6I4do4dohEHz9TU8yKM6jrV3fzixopJMovMQN4Fx6AiZlmWZE1VFRHvOXZdjBHIh1AysaRlRSQiYo49LueBERGQTLOqqvOOi1AYsOO+nU4x4HIKhQAA/eaPCD0ir98tkYiIRLJmI2JAElEmQuTe5E9sMWfJQuTaJhIgMiUTsz58rohAy5OFDSyrIhmKOe5v5NjrCQBkhiGUKSYwIl72MqSUAXCZUhBDwhBCf1gZAAM4ZlV9UDShSIhgogZoCsZMxByY+m56a9svfOb5119+r2snVBybAENQFSJSBPKsScix6+LzLdPq5HfvvyXJr2xs+UGpXY2MxMoee8z3iZ2Td2+9+9iZs8f7u3Z2FWbRZ6lWV51l8PKx2+7C9w7feOWdn7//4v/hv/pReMIcsCU1JuzSj/mtupp2BfpYhzMnL7+/f+nH5xcvPbx77dVH11fkzb2u8g1oLFwLIISi2YgNvUSRRogAp4lAkSo2iLOsmMwlAgrAPqyU6rvcDcvhsEhpngNgwWXuusohaPSOytVysahdcL4IOWvqkqYOlUajlb17965NZ1snN0+sVO9++P7Rwt29t7t9fr2p54iQUir8gJkNDZEUs5MMwEYI5KoQrKzMLIu1TZMll1WRVDXH2EU09MyqAghMjISSsy+8mTp0hYj0G6epMkO/4Pqr5NLGQsv5qaoiGqMhsRkisHNgaiIq1pNjtN/Z0ZCA+j835owKznNMcdmm1+sfpJYMgFVVlkc2ghkziwmYMTu1fnqgpmYqCO4B4M/3kzRD7WmXEjMisnOgZmY5ZTNTVeecqkLPayYh8wCUJWXrTBEwaJw/+ej5lNp33r1LoyqbsQ6cWUO9AgTWGjgWsgsH3Xoo//XdD2NYxaO4Oi4jQ6fIhBMaF+A8aDDnJ368VkE7812+ef344Ye31nI165Dd/LO7cPq2T6v+rYOb/nDxe7/wxrde++Av/tUvj7gy0Pvf/KOb16/VlZ3ZC2bFwf1bN6+81P6Fn6vsggzOndvudt5/cVCMarI5aldC4zASJh8WQY7YGuZoIIEPWRMigI/s2SzUqS4UNA8wtK0ZhDpntey9W6bhxELwOccuQ1Ey5VCGgsnmMh+PBxzAzDCklZPD7YBdbBaZPvNDn3nj9Ws3Prwapwc3rn/UpDpLzHXNXDCoQWZGIN931kjKohkQnWcfoCgGBqhqWTQlwkGlKeWsJJZVF00noqrI3gOaI3Z9HJBIAYDRRASX3Y68HJcSmar3jIgiCcD6kSOTIwAgQVnedvt6XRGhZYILkEhSIiLsc1SyNAIYLAsmk4iBMePSO2sgoI6cZlXpw1VgoOxcyilqR8xL9B1inzAOIeS6HgxHOaUUExISUFbp7cA59/9LFBEuCJYqVnYOJCuhkS2eefpzv/3bXxMrc4wGoDGqWeszizljEJhhHh+3zxZre93sg3qRYmWsrnBt15qqRahROtYFxOgtSX3m3JbtzlfPnHzt6r3Hzm42i+NFZ0+sFc+XmD+x8uLB7d949f09krXhcfM778hf/GFbKRJIlwTCeP/+jVXaqsL+BjxU3ruxe+3N9UeeKZovbD79yJXV3y0XiWJYE4BpMhVTJpybteoLY5cEksd5SYmtDdANBm8OdBpwoDw1TdnUsplFVSKUGBGJGLJgbqR3HqGBgWtiYiSCcjpvjQEJ9w6PneOxG40J5wdHB+X++Z2d4jPPN0dHv//732qb5nje+BLUWslSFcEzISKoEXMgZs9JcpuyZ09EIkpEnsn1cHUX+thJlzpfFKqQsvZlWI6IRNX+nXAqITEzEy9dTw8EywcWE1pKjD0ioEegm4GaY1YTMiDugwiwTBr0uhEiORKUfrHmJH0C3dkSRN3j/JbxF0DkZUYWEbOIpjQZT7LmpmmcY8nSv6IIqWs7RGqbtr+BmJh/AApPKTHz0oxDZFmRtSi9CJlK4QgVPvnZp27dvX5wBL6SHEtwrStFsyPEkE0coimoXGro9GTw67fev72IyfnMaW1rB8mrt84EOQ/MttBh06XYnvTDd25/tPL4xYmX9u7+hR9+7tT26jM0bPffXr1AR7/S3ppbVQ3f+ODaJz69MxquqKFl9cNJtbkJe9dnA6Mx6Dz6ubt++a1HLs6+/zsvfexj57uS483jXLmsPTY/BxejkeEoRMEkhkKiYZZXqDz21h7ep6dPfeepVd1vhwJI6JiySEqK5FSVSFIUM++dMxARqZu2DzBlNUQKPFAzNBwt59sKaoPR1mL32Gw2GZRgza2bB4PBCqKY5aaZOw7D4bqpxq5ZzBdFKBYxFYOyGlTzRW3M3rP0XZpoiCg5A3kD67qGmUZV2bZdwW5QFCLZxZQcLX+cTIRo7GmJYQFQU8dOVbOqSFq6sbHv6c3A5tijojGCgZkxMxoiIRKmnJfZQkQw649aM/OeU0rEyOhUrSzLH1gAc8799wFUiXi5e5o51SyiaM77oicjeOd7CJctA1e2RB9x/0mIqM+7iAgiAkKvSgCaSCTyqqYSx6Pw0OkTv/ab3yhH60AtAYo6FxjLAlsjEUCykk8eNBclpHa6l2Tfigqccry7N/NtKp0Sq8Mw1jAcb+t43Q1PnT5zevLQme++99Hf+O/+j//87/zjj338h5q1wB8e7eV3hzdqfy8Om4TejbE6fWLVFSFGwGRgdDCfj8jB0FUXHqIsm+Xw+ouX3c8sXvj8Gpc3dn7qofpbU0jqFyDTjF1oGzLJmJuEhAwKikCDYnDQ6u+1R8+d3Fr1VT5YJF8UlrLPiL70nohwmZVLWVQVxKSf3yARs1OV1CMUVYL3orlndSMZA+VOShcAoWsaJCvDOLaC5OIieSBCOLi/Z6bswHv2nhC4i60vPBE0bd204D3HaN77siyBOWdLsQMwInTOoYGIZAXoSRg5Z1jucMthDgKIaM9j0azOMRNjD8c1kKzINBgMUo45i4oBoaqCQc8sZSVR60dwRERLPJwhIjPG2JkJIvcsGO0XEICZBe9F1JaBXFwSYhB8UQTEtusAYDAcdl2nJo49wDJK0IsD/ZneH+j0AKgcY+yXLyETkUCXLTMXDqiZ7//wD33sxpUbuRu5iSdgX0iOk2TRLJcYeMUVmY4Lu3S32wY6lNmiaSOEMmsoB3v7dbs/HTArO5kkOZwWZlvDwfzu/Su7U1vZ/Oi93bcuf9Rl/LVf+pWf/Ct/qmE4dfH5bpUWb/HJV27iQ+Pmrm6dm/SzH8x46+atGNOEXC2tdIuFz+NqRS/fT3m6eW4HYW7//qN2pglCXEeJMp0r8NrKoCzz/OC9O/bB8cq0unuUXp7PX8qHj29uvZD879K04eC08uC7lIrKGInIJKtKJlIiFFA1RV1OpA0UGU2JgUGzAwf5Qf8HQ5QMSG1MhDocDbsmzerGVd4URYjJQdZQopnmLOWoKMsSgrQpdV3rvR8Nq6ZpegFxMZ9pFgBg5LLwRKGuazUbjsaxSyIZwJxm6Xc+VTXLfcg/a2JCBcg5E4IIWG9WZQeESBq8AwaPLCkygooRACKrKDBm1T6UvgQoiCARMWs/TTXNPYmIClNLKTJTT+FefjmIGdGzyygI3CdgUk59P31OmfskcVYCFFO15Lzv314pL1+CgA/AL8GZWu9NIVVgY56gVWTT8+dW18aDb31rf7AyAjZCz0xE6iQgQrlSHE+vtby2ea99pOZra47m9tbd47tKLeBgrRr7MLEqhGAICeHYoT/sMHVbzRR3m6/vvbrdTN/8B78021h9/Y9evX79ztnVtYNRdebCzjAMBuO18YnT7eL+1s4JAIdUz1UXV+swv+8InYk0Ls1lpkM4uHe0f3V96zFDA6lmb9+fVIN5icPV8fjksDy5k2WeYq7OnuQf2vRKcmX6nV+97vf48963j1V4Zq3cryui1rWMpQl1kkTVeQeGmjIDooKRIT8goZoy+coXmsUAcpetx3ygWgaPnr3vrDOz6aIhZOeZVIlcKB0AVuUQ0JAGPVp80SZTZSZmFoG2sZwJQUPwRVARJeasEcQsK6IxAIF6B8AYY3JE2G9YomLaFzggmabllo6mOYk554i4rxwlwK5rk2QgYCRCZu7p+WbEvcmPYanTmmqfj+radtllCUvN0kDAjBz1HAEFzaJIxIRFCKrSIw3UQHK2PiNsfU8wIiIHn0UI2ARE9UGeCw1RRKzXFFRDCF3qVBWRBUlEQDsm6tL0+c986a33rjSdGww55jqmBglKX4ayarDTonrsiWeHOzurX3uVdqqPzqw9+tI7w7E7ZaWLTexm+/t3CmUNPqsyorRRO33xzXe+MpqoSLE+2D62TwxO/dPm7v0KV6/v0e35G3X3jd/+xkOuaBXefe3N00qnH94RyC6Ue+99NL+2v4WcFqkaFBqLe/P5w598/r07V9q7u7zzVGeK504Uj14K5BLn2LU06/aPLvuYtQHCKgEe0fFgy/7sX3qhrKtzpx9Z+clPfHXmbv+Nf3rU5W40HDUMTsT1hA0lIix8f0ihw5hif0tAJUIyMg5OMzIzM6WU0RECqmgWmYyGotrFLquWoTCVpq598G3bak5m1sUUQnDeqVoRnGTr7xVKBIBi0MXEzqeUDaAsi/4HxOSYXUpJRDNk59mFwP2Y0LvQXzoRMPeIfeqlTYeoAJZyMku6nOJID4cnIALzgQFAJIcQ+tOW2dvy3+xlAHLOJTEA4KWqmlXF0FCJmIgJl7onqEnXNmD9f7ysQUAiBmBAXbJRkJ1TM+qhSLbsPUfU/tJpZv0v6rrut2tCAmXvqraJx/M7D1/a8mX1yqvvBF5JKVWjYmO4sjLZGFYb4+Hq69cOhTZ1e4ff+t74g8XtL1+aTLbX8fon1jceik6gwLLCrbMx5q6rp3Vdt3GPLZZA+0ePdeVkt3nU+Q7C8PjgTwrc8TAeVofN4n7QRY5rw8Erd/f90A9H1anz6wYJYMRHMbQgo3G9EES+sb9Pqu+8+BoKXnv7/bPPfRmSlOvbw8c/vdjdn4MQNJw6MFU+KLakTTBY29rZvOTWBpPtrbC5mb23HLfGmyee3b75x+87WHUsgn2DB5pZSgkJ1YzAsSI7byrM5LzXlJXEFyxMoJBSRHapS6oayqLfS9CsKgrp2zgVB8Oh976qBn0ybrTsqUFQ6GsaiTGnpJrYsUOnKk3bMZOI1E1OKSEQLUuSHpCDEJ2J9nWf3nOv6puZpz7ZpGbGAH3DkwkAMDyoMOw1UiZHBkBgaszYb1vwoImk9xgwu/6driiIpFkIuQhVyh2oqPLyEeNd37/qfWAUpsIMdBlv7bNUhog/8DxpjstVB2h9XR0zEWNPWuwvowZlWWbJWbKBgkibYxisnt3Z+finLr79yssvPPFstbI2Gk/YVyKu7bCe5Xt3u6GMrx8e39298+PvvRFK9/rxjfVrb7ZD42Z4MtCMIFRrFz7x2VkXNS3alNL+0f69ewyyVeLmft1ivZNc8qhOPy80dZRjLK34Ulk0Q9+An41SA1o6sJBZUYnh/qK9c0BPn6pLjhTFuY0Y4sEhJTm4cgNAnIw9N3H3Wrc71aoqXYCq4gJGqyeL9cmJMZUTV4wroEEGaJOE1rMysjz19MNv/vEHGTH6ZHk5C3TEYgCA7MhTUS8WvnRqoCZJLThfFKX00BtCYAKDqiiVABBIjQjBLJsSgqoQkiuKHnDZyybQU21E2TES5ZgMCNkhZFHR/sgD7iVqM0ByWUS61N82+zGhiLp+IAVAOS/N3UtulRkQLtcj9r8vCrlnExP14WACUwPVbKp9iJoQCQGZWUTNNGeNsSUkBQUSU1AxQY0pMoEkcY57PchAkJgIJWexfnrmVAyIRAwBM2RAI1gWpjomR5RU+o3a9X8riD1YEtB84VNKWbOZWdY2NsXQn3/40ZMPPbG2tVKEo9MXzsICYwoqHDs8PFqMRiuDMhRFsRbqwtne+/f97dsHZx/H3YptOgMqBut5/z66XIXQNCkBEQ4XsGgKnjI2B7NHi82Qu2lykMhCWXSyqOct4iBTRhc6dWWOCEmyI1xZrYohRxUmvnP11lA5Nt1kWOx1sxGUPhuUNNGh3px2TY1ujJzWzu6o6OrWxmBYulWPKziZrACI8w7AN3O5e+PGYDLaPLGOlhQJsN48Pd4cTRZW1pa6PgWkoFn7vC8i5pQYEdQAQQ1URQ0KohhTl+Lm9lbOeXY05YA55zZ2A18AYBe7LEKeu5h6DdU5p2ZFCEUo2tj2LUKmfYBBuYeVUpZskvsVZf0dsrc1MTswFOkpUEZoIaBDZlsiUJZVDEgciMwsZzEwAQDA3tvLvNRKmQWAREF6pqaIY/aufxHlnHIWZ2Zd1/XXzSVDotcj0YjMIYqYgYkYYN9LHGKMDgkBFRgJ0djQCAn7YgxVQGDnLCkiSlbDmEX7WKCAWW93ZWcmDp1CDoVLORnj+vbWqfPnxmd2BjRJEvfmR8RhZ+NsMXpHZXDU5rXVM8U8s9axUcaBTNx2OHn63GqT9mRYnd3awX13f3ot1/M7izl3lqX27ZxHWznFAfg8mRy+d+3bt259vplh1wxTJUE5tqAqwRURgXwCaS2VXb5JSZIC+bVTw0k4kfOCnb157e6YZDjVuE7WcezIVAZdXqR4c/84Ht0uTyIkGj0yKs4+HMoBVwExEdjuzdnW9tr71+7t363bPUnd4qlPD5AzCKh5tjwYFSizhTFEh5iyLJ/FKYuIlgUnzEkzZmJyWTJ714loXBBSCH56dJxSAoAmNYNqYGZtl5idojMGJPLeEzsxZedyzqIqKp5cjDEUQVRTTgDAVdE2jWnuuqRAIRQxJXJkZtwj/EXRDBmd52wSuAAD19uQeTm5hqZpVLV/zT4YdhsilGXIubcAab/giFhAydQxKi7FeDVQYGTs7+DeF73WA9DzWpUZQygBAAxyyoBsCIY9HkgJkR3nlFWVgLJmAEh9ETIhU+i6COnB+8cyE+WcAKG/+5pZ28/yCs8Om6PGFNZ2Tu08+jhvbBxFuXkXoO0Ck4BHSfUA1twWQmPl1u2jkfr1UEny09GooEaHFsfb4+P/4idWLV2YHjQLooN0uG+nDnxzEO8dSILD0q0BYGmrRee6oJvPPbohw9bSnZu31pqaXcVCACZkGkStK1OBw9HVo/vNMPjp/MTmwJWY2gpUz1OBQ20Xi+Tdmc2H/Ob6/t0rjbSnLp399BeeLId3pWuZN4sBDwYlQBBDosHB7v0Xv/fexvbJvZsH9SwOJpOVnUEYEmgy8qQJDYdhY1ANY9ORg9KXXYwiklIMoQyBFouF94VmGxZF17UOUXNiNBVrY1tVw/4JKyKaJXLXNxxlicRMBjmnXpZU1Zxz7ybLTUMABBhj7BNIIlLXNSKS8w4YAYm5JFRRIGBHaJCziPSOemJAATGw3lRnXdfBA1xOCMGySurL8JZwKyYqirB8Hln/6PEqoBIRNYSw7AZTEABm6lGw3nvVpbPOO2Yy57z3oWnaFLNI7rqInswMAUUEACIiMRmgdqnHqhNiz/pCxBwjF+iDDyHkHLuuKUrv/TL20FM0+ydkTFLtbJ1/7LH1k2dT5sOjtlI3mihOTFSQeTwqWKZN7QZ17BYH89RyNY6MBwcHjkI5HN6a59UcVzfibr1ITKMV9/yzJyetnccZ+Ob4+FhlN+s8ywBzkVT8idW1s89sNrvze7fje+v7V7p8PHczC8JkZhnIwUB9Z9gkKz340oYn1gE0ho5qPdcNi52TV/ZuXjm+tvrM9spT/tTa01tn1lfPrsGwyMIB1xScYZMFiJ0YM/mm0QunLh4cHqnUJ85MHnv6gitoXHBqgSoUrlGdK4fkuTs4GhaTnHPOmZm9DyICoM45ZiqpzCmXISAhkuWUkunq6loIxWw2I+LeemZmOUfvHSKaakxK5BBQsojkmJcvfVBjoh5tZxCdc4gIqk3bcgje+dhGZxLYJ1VmjjmDgWeXs6SUmqhquXSFmbmUEwoCLO+2g8FARfQB+b3vAVYVMFS1HiHZn+OqJtlUhBgoJ4Al2IQJTXU5ELVMCKF0OefgvSllsS63TdMAIhG6MigII6ECmSoYexawJZKQuX/t9/ddAizLEolFtG0aBREUSZqSMhNyH6ISUFndPHP2kec3T69n7mJeMMnOCuRWklrGFlxR+IHFfcOMYTwcV0OMa9ihHTDAhY31uh37sHthzc0XdZR7jcGHL980PXyduhHRE0/szGj/zJmV7SoIZsGDUeFnbbO6vVENpel2T6Je/OL5WTzR3pt3Nxf5Tlrc7tp9i4vufpZZmD60Otpo83ePD8ePbAMwY9ksju/Xt9xDxZN/4pOPnf3U8OTOcLUI45H4shNBZcaRdmOgRL4GcAqgJGLd+ubo9e+/+fSnnnt+65TcjfhBbI+1+1gxeHiS6rY7ujw4cdYNJq6iclAUMFLM5Jzk7L1PKTvnVbXponeM5FIWMoVsAOicz1lyronAe5ezpCghFEw9U0mZfckhRfGefQBvWpiI6LK8S8FEgDCJIID3PhD5onDoAaF0HkFFNRRexPrRPCATM3vn0drYGiECueC9mRGxganqoq5VBLGP35rrrSQ9cJSsj1M9wJ8aERMzImTJRLh8wQGQc+SQiPsbSUqSsnax0WxERMz9Q1LJVLLzrleS0GNglzUH55SXfTC9CrcU7RRBICdlx8lEkRwPkNQR5iw5SlIohqPHnnxi59zJ466u21soioZN1NmiYQ6OSGmRYzuLdVk0MTeG46MCTbQicgIS97k4GKyuliylT4NJKrq5nGgfPk8sJ96/dh9rd/3KvQW6V75zdWdYbJ+aDLbCcBS3VgfV+IhzzJl2RVcrBa5Hj4SNJ1dSZ5a8ZO6mXdz3h/NjuXpYZPTT/OiTm5Abb0Mie/jnnnfPXhxtTaphRVIauJgdNNk8O2VKrhXjhOw4G4I4dpzsqBoMfuTHPtO+v3f47a5YGeg2TD62gQ3c+dU33v3Df/OFv/bDlrvbb7/qRDSaOO1iq2BFUaiqAbBzuWs5oA8OAM0UkVWVmVXFe0+ETVMTkUjszT3O+b69pG07VSuKypaQW6jKYeo6QjQVyQLeE2KB1NfXd10kIiAzMGRUhb6eImv2wYFRVoHeS046GAwANMboUhsBEFhl6dgAcs7Uurjw3ncp+X4BkQGAc56W0Gj7QTITAH2glJKqAmHKZlmskx5LKQbeezEEszIsT2EAMhVEJCDJvUkPAVBUEcgUTY2JEbn3/ifVlBIjZc3onJE6ox6lljRzwewZnTt//pGTF85bIbtHB9jhZNUrRIFcBC2DeJcAGAyYupSmasMuVaHKksN8OheUYkiDlROapT6s792el2WxUgXnfFnUVUnG8tzjp1WlTaMihA8/cvuHbUz5gxdvnTl35psf3DDIn/nc42VJRUnX7tw7f/5EF+dNO3UuIMXBii9XxE7rmXKzy6uu8p9Y+eSgKnOzwFIEZO2LHx9O1kQNOlC2xHNXbFE3VVeSeJkHToUmm1k3XCshHUNaNa9Keu2b75ykU9tfONEUB/HqnRv/4gP+oN772jce/g9PV2cv/NH//p+d/6mPhWpQcMGupc6QiYhSVkNoUxKzUIQYEyMCWIxtrxyqJGkEEVWhrlszYCa11BeXImIogoi03aIsC0KQnuKsIGgpCRKoCJNjopyzmTKyYyd9jbSiGSIHZlLEwnsAVDVVFclq6JA0SeUC/j//6z9rS3wOPgCSWN/tw8x9C0XhnPcegFRBVZh5eV+M0R4wn/p5Q4yJmUVMzZZuIyJm7mGWg6KIOWdVMQEDR6QigCQq3of+idN/uQHAVAsfzExN+vtlNl2CfdTGRYUGSboeJTDZGD329OPVaGiaCDtUCeSBIvYZAjXAjAhgTK5DZISBSv/3XPiw8B5UJecOEZtFI6KLuY4GoxTb4FzsZlkOq1FCm3lvVVmI5KIoCVyKhj62Ee7dO778zsFosEOsH35we3WtilG+/OXHN7fJrCFwoMwckL0Pg8F4Uk6GfjgAXxgUwQYzqTWnUTWBjMoC0DrE2TGE9dHetXfKQQH1w4Wd985fP/5wdCLvbBxSxgwVZl1M68n2Y7f/8N27f/f3zuZT7Wijvvleu//GQ//yP7n7d79dX24+9j//1N//a//mldlgOIiVVUVVNm0bYwLEXhgChJwzPgBAI1PbxV7MFLEQgim0bVuWlarmnJi5rhuRXBQFAEruisIDsRqSYzVzzqXYBeb+spZFzJQdmymxFxHua+8euNWoz/eGQtVyTkUREDFZbGPryHHffcFMiBS8B8SUExHGLjJzv3unlFOWnpqTshChqRFTD+BkYjGQrkPE1P8Ou15g6l9wfc647jpyrrfGIGCfRgYEVet9cd77FFNRloggIvLARwKIwXmBXJkbCGWC5CmCWJ2Z/NqJrUefOMeFmR17Us8aWFSPAY2ITF1RlllANLrAmspsAJbLEMqqM75f4hDJqamaiymXxTCnPB4iEsZobTfb3FifHhWSrJ4fbm5O7u8dd+3x6krpPYxGJZiN3WD7ibUXnr/QdTCbz37kK0/euXv/8vsHGzub1SCZDghL54Y+lGFQVsMJhQIcZULDgBoSdQ6lIutwUbixknQyBSxf/Ie/ijfleHrvi/+PP73f3ddsZ88M335tr7kenjznnnm8NZ0DuMl2efu7f9D9/Aej28W/trc/vn7C3r/sf+ri0e++2/1Ovf1/+ZwOivVibXBwt80T7ep5UxNT03T9g7g3vYUQYtOGIoho7KL0RX4qquqd7xM1XdciErPLOfeRSVX1IXQ5eSUEaroGnFezRLkXWZmQ0Dw7yRkJNVPbdKra59H7hEa/QEVE1LoudV3rHIfgi8GYgHpENJBBzhnMNKcQQs4ppczEIskUFBlsGTl+oFAEYu6JAIQmagxkRqpKDEjQxc45VlGHHg0LX5pZRvOFA1o66ySKI4eERRH6e61zjAgiKYsQkkMS0RgbH7wiDIDnqVt4R7XObhwWBV586rFqu9g+WZV+yhR7W4BBRjQGVlNEygp37u+rts45lnEnu2IUXHmo9c724OSJ7ZRmKc3NiMgVnhCBiqLtFG1tMlhv0n3ke+VwanEoxuU4YNjo2lHKaTicVKMRkSCZ6amU1IW4uUFEeOnS2Wc/Ac18blm9KzmUfuBdSRQcYpGh74hnEO+hTBTNGlMzHPzBf/+P13fWH/sLX11As33UyG/eXf/8I2unTr/73VvRvNL0w+PkJ6NX3rn/2MNjVzRKEBs7/H/9L+Wbw/8h7f+r+e7P3dr7SyulLGb1b6bqM89Pnj/n3IQvrsc7e6OktWUwNLCyLBEx5wxqyVREQGSxSLlPlZkyYgiemHOOquAcxRiJSE1DcCkn50jVcm5d8JlZNXPpiMgb5SRAVDcdAIimoghoKJ2sr63vHx2gAZFbwmmYDTBLVgAVI3ZFORBNMec8mzI6Jw9I0jGlIoScpes67z2qqhog9k1LqoYoiEYIg6qIMakk7wMzJcj9+AfBvKPV1ZUuxrprHXnNAmrM7ByLmonmLpqpqYyrikd8fDQlI+gRFGbLcFzKJpaki5ZDWZIv9+f14fT+om7vH84Pa/Dd/M+d3rlw5vTkoRO8PivLQ0L09ICVhr2pOiEQs793fHCwe7gyOXnl6v3xUDa2ygsPT8pSrn209+qrH+LHPlZWWJQFk5pmQkJgNCirrl7s5rYtCiZfjIcnTWhdhgp5ZX1ItMKOAFQ1ibCSMrQEhuDICuBGsa1n4ENVTioKBQdGZ+go9z5CCAiIZsQqMiUBL6tWzne/9zb/5ofd5p3Zn/hE2F5dNH5aHD/0kw8bpDsH/npj28fNoFwvuYVQCDJ3I6zgxr95Bd7Nv9nufqfZ36gG357tfXn11PmrWq6e1C0J2+sKYfv82H3Ta9LJZNglOZoeAzIDmUII3jufcurBJOxcH6k3xCa2farWhwBowNR20TmfRauqbJqmN5MyORNd6jkEKXfe+dQ1sU0+eGKfM/YQjsODA+cQ2RmaiDhHZqaalZb+Hu89k0NBRPDoJYsrQ2GmiFYU3jm3WLTMSyflsgAdkQOY5r5eCQCQ0TtyzhFilOyd39hYv3v7DjMTYFu3xFz4EkSLovTe55xFs1g/dBTnEIiCd875ENgUe4Egpc5UVTSEkFJSzwLUmnvr/dvvfHBjEbMZRQuB6P/65c8/dXgFP71+OLh5hiFTMiPRpfeUgBAUAVCdSr295U+dOtkmHa4URRmCxxtXPqjni+2tU4898ujdOwfXrl09e3brY88/Cr4xSAQZgdXc+gaZzcFIjcwUWYfsAYJZ/2wVRPQUmADAkRn1wTxMyIX3hQvBlx49gsMlTFAZkBGYHjRfZEtIWbBiHRI0e99563DejhHTe3srW+Mjnoc/99zZ/+AFtMNOM49p6Fyy/ZzlSFYEaqa6aau9X/ze6nB78fQwfu071KZZ8LdS/anqoaM4CqeGYVhI9NxogZ1W48ViYUhlVYGRGaaURRQIYoz4AwfJ0mqDKRsipiRdVyMxIjr2MeWyLJBIen4JQOqSI0fAZoDAjiDF6J1zFYoBAIiqgFGPKJAlZpGZzTIhBufR+5Ri0zRdV3sfEDCLAlLO4voXiYgiokjua3LanBDRMVdlaSIqWrDvQf+A2HUZzHLWlBMZmXbNrO3pdmIKTTcaDlPbAYCqOOfIsSJ2XZQuheBFSTUfHBwCILOLOTn2BtYDpjNkVUGHMSVBd+PWvVdef7fOjC4ok826z371/IVH6kJP6NOjnbLz4BkKIkSEBy56BkAABktmWcFEQuW3H72wNlqbV4Xv2h1CzZIODo42o338k59mR2oRDBEKMDZzgBnA+sLsvtUQjQx7HDYRLdnAy80aAJF69Cl754J3PphnMJO+OLTPT0FmQ0AxRFU1JUbHUAJA6+uybg/ev37+r//E9X/xdX+76br23I8/9cSPfrFt9lLXnp+E1Axz544bnQAF27c4wMnK0TdfX33r8M4jD89OVqe2Nz64dXVejAOfJl6Z7944vXUJuLj36y/Of/9aV1JKizjvXFF479sukvOI0Hd4EmHPWfHe96IeKAyLgRlUvshZ+kBwm5IPHgD27u85zzkJO0aEtl0Qs/M+9l3yzOQcoDFgz0uEZfrbccaUYlFWZppSMuKuy5CEGEeDIQF655q6IQBiyzm7B2EJ7ENI6NjA/APObc657xrpl3zOywAmADADWh9S0aWhPee+c/Lw8AgAHSGixtwSezGsyoElbdu2BzL2nmrJyQCSJYS+URhVVLKi0qgadSpxPn3u8fOD0Wi2OJylVg7y5z/56MYXt1G6iM06EyD43g3LREyEwA7NwNQIPIADMjF0nNY2fUoDicqwmlMCaLe3K2TJKWdJBYd+FAVmZrlnU+CDpjkAt+w5BfjB0gQA6L8VTBw8e++cY79MqvTiQy9a99AJUwXtspn1XR+EjkAlcuyKEd756MOtx88/85/99OzdN+7PDk9klfHEADHm7NfW2NXN0XFlG6P4Ixew2sh+0AoU93/jPZc2X3LN3bvtaG3D7d3ZtHCax0fvvz89P/vEn/zZm6+/eutffOcuFFS30UoOlWRRzZIldokcmfMiCQBEkqoWIWQRxD4AwuS4bRoiZuc0xRAKA+vS8iFrqoRklquqIqI2djEnMECkOJuVZWEGTMzOtV2LjESEmRBxsVgUReGc74tsiMjUupyqopScBlUV29hZRiYnORlgTiIu9E0L7MgZGICYpZT6bl0AzCKqBob9lwwRmZxgVkA1RYbSFylnUzXiDJglMSzLSjRr10TTLJLNegwzA6KIObYflASaGRqJaAJJzZzZbayvtov01OMXVRaOcSrku7Wop2N+9VQ5xhBEGiRyzOzIMSKjmRErIrAFAEYWsVQOu6w30QVyApoCgqnLmUWDW8rHhmjO985rMQuAqJp7UrUZItAD28CDvZPIsWPvzDM5BgJDEBMwAQMyAGRAQmTNWVMCFSJzrkIOhh5ARBvNLYNjSLHp1j/+VNJ7w82h1eiBr//dr+m19pn/6NH53RmMqxdWF6uFXnqk3Ls7P7otJ9ap/nCav7Ffr/vLumgOeVKsUKJZkNuL+2eO5qf+T1+9/+a1W/+3b+49+vg7ux+ySakratE7tZ5r1B8xIr0RCRBTjFmEmc0sSWq6GpGdd7nrOMfeQ6ZqZhkdoykCAGNVFTkndlhxVVgpIgAYQvDscspMLsXs0EdJbdd4YOdcytlMemFTVJwrJGVTMYMcoyPn++wGkmuT9CU1Obem5nxIUcGUCBmQFByyKWST/oLIiKBZergFMRM4JADKorHL5B2SihoBEDrNGYBzFjWLuWUw513vbMqmTEwOk9hSijJwS3QUlw4UkVwJeHw0P5zWs9S1hYNFvSja9Te+g4Lb0xO7p84NJmNSicIZbWDIhEQAjITQw9zIrCjLsqmbwbhkD6aE7ESUPPpgrJEATWk57VczYzPHgIbgIPQ9a8v0HkqRoXWOgKjw6MAz5UBoACKWDBWyqIIyMTJFsiDCTRIz88zOZ6cOS7CNxAcE90NLBCl5KmCk7Pym92TJyskmplk7fP3o6P632i8/4jfCZp4PF+u3Xp7+k//l8uMvnB/Q3QtwavcP369uLW4/c7aHxC202RyvdOlwzxbFZPXuL7315tXfWLv07NuHu9yWGVeQa0rK5A1IVcFQNRbeVeOqazsiKr2vm2Y8HM3nCxUNofDO5ZT7gueUMxEyMrDPKibmmOKitYrmddsbLZf2CSZEzJI5+NRFdgwqBbhBUTjmGONwWIqIcyFnTTGrKINzoUixA+Kk0uUE7LucHcADVoIKe6ciANSnh5Fcf5yZWs4JEZGAuXddAhGh4xw7RgIwFTAz6aKY9OI8mJlaVkFeupn6o5iI1Ewkp5Sc98QOEU0EwNgvm5g5FJJyG3vlVVS64NSFzfZYjtPR4+uDw/npq1c3rr//+pd+psUwmgy2TcSkSSLOFWglkwM2YjYLSfB4Ol/bWs/WokMyWXpKAZDJjAyNebmFg2rvcXhw3CvhkhzUEYtHICh8YQgaKGftkoQEJoJZ0frBBBkodLnK2CFkByU7cSABK5pkIGxvV4s6e822PN4AUAiAAQDhuAuPF91iWhJoi+1skTr37ksra6cuHbbff/3N97/y0585dXZSz+Z3fufFjQHeiq2Oqon3ydtzn37+BZmduzHVm/Xo5cXK556/vErXbt1sVjbdVGpfrxTDupmFUAKQqBBCzjFFXXYXIXnnmnrhnWM3VFXHzEimKqqD0bCeNcyIyKb24ESFed1ov68gM3MX2z700Il41Sw5W+6phqmLCRFMI0KMcTTyANB1rSsKROi6WBRe+9gPOzFxxD2ow0QAAFNKhIyoknM249Kxc5oSmJUuGFjWB4YoxKZt+xSoqABgWQxijKYWnIs5O+cMAPhBrakZMzvveqyDmqFB8B4RezT5aFgtFouUIxOr2WI+NaBisBKq4byzw9oW8/n9+1c9rw8H7WeLdqB+6Mn5sqgGv/DzX/v4848887GV7RPbGAYtmAihmEqNrnGuNPHlqIQ+3wT9slwWdSOR2bLDtOdJ4HKwpv1HJwUTSzkSYoWVlYEq7pnq0rS80EItgxHYciQCJilKygwAMAwhpMoajVUYdAvcu3wYfFp5Wrucqm6UiilrwRZyTuPRsBqPOk2LW3sr57743u2b17cGE+mGYfjdty7fvR/OPh0+9zPlZ3/ip70/oNLN3rrnXjzOo4v+1FoV2nGHB/Xxjf2b5xFPJGsGFk6eKI41xqyuoJSmXrelarpIjGpZVJmYzNrY5ZiImIjMtIe4i2YmF7sECuydLyp2LkpE7/p7jaqe2Nk5Pj5umwb/ndq4nGRQDE1VVZxDMAghOPfAHEzUoyF6TEFdN6oKSCmn3k6Ucza1wA5UVJTYOer7Vc0QyaPvRcp+eWWVpmsYmczKUEaJDIDEXdshIfZ6DsD62trhwVFd14SEhKrqiDT3uTVNKTGxYybAlLMye+8r92/3ZjWNMcauQ0R2npnRlIBnTfzg8rWPbu3uHi/ev/dazELT5r/9m49/cPnmr/2LXz5x6uKTj7xwYmsVh8X/9Bu7v/qvji881Dz12NbHnz339BObp86fDmuu4KFKpVZ1ZlXRxTwnYMK+thOXDx3sQy2IfXpq+eKG3gjL/d3UemVJomYsi+a9Wze+9t0Ljz2Bj52UUXCiSGgIyr0OpxgwuAJcyFyxIejCh9X6/fa1f/SNtfdkeuWtrb/+6XN/5tMy3Uc1BAUmSd3O5pquTOYf3po3bXF69NDJ9Yv/9K++9Df/fmr40Reee/6Td8drB62VZQ4La9D83jcvj1t32+9fM5isbI+EVq1bGVYbKY12j4fFQO827w7jd05jdgGablC4GSkYMmIWCd73WZrCV+R83dSoCgBZs2NKORcBo0rXJGvAOZdFyBEYqiZSMdX9/X1E9CEAIWsmQBVAxqyJkfqDtPcPxRj7e60BMAcRUFNAAsTgi5yzKLiCY4y9T6BLCVSSkZo4U1m2K/Q/MUTnvJj2648de3YOMHWxT3owEgIKKIJ1KRHh8XRK3hFBigmNSuey9CFM9C4UZcn9J1UT6F0tXVPXvcmeCK038qWOvUeysigODg/evTF9/+rtvaNjASbn0ZQojKt48TyNi9PV54t5ffDea7964dFTG6sXHn189a2Xu5uv1X/00t7Krx6dOtk9cn7ryUdXn3z69Lnzk5NnN8c7O8MwgGKQs2WVZcqbqKer9Y3my/pKfFDR94MKb1MARCIDZDEAnL72wfq7R7uvfyt99pGH/9QXAKMSAQE4REYiJMRMqEJlxo4FwqC9Vb/6t/7VxXtbzezGWOnwF14/+6Un6lPtqB6JL4SNsiCKYXnvG69snT3Nm+NhG5uAw5ObXMD22jzDQOs14E4sWxjE/Zy+dq3QiatWTzz+2LypB9Vgcjw9OJ5q5bnD1MZXz47/4HSAzrmsTQUuZwaK6EQV1Nq2A8PCV3XTuApDKHrSEAKKZgCIksk7713qkop4ZjIEx8RuPp2q6sbGxmw2N4SU2vFoQEhRsnMuJiEi533bts773hHfdyTEmPrCZgBQEe899WWyYszs2TFBFukFqVw3o7J0RC6UIaUEYGBiAKLZzFSyATjmlLMQAQEBiagrPUhmI48YghMQI3DMpuiCM1WCnnJHpopEVSjatu26VkQFmHjpnAcA7I9UZgQtqsr56u7h7Duvvnntzu7eNBOxcyVYRsuEJKK+1MND+fDD6z/5E5/f3Cq/9FnFYtYddr/w//5Tr7x6949f+ujlV298+OHhO1fljcv3fusbh+PxRxvruLECX/qhJ7/ypQvjzcGZMzuDlTEMhoAMQAJZDCGzSkYCAEUAQ0ZDoWjmCRQNCUPOaijMYxdlNr23gV1YXd3++LNc+gQtxuQH3Pgq5IEBt5CqxImpdVZYyC2+/bf+9c7t8ay9+X44jKPmkVvd9Mpdf3YdBaxIpCUlrSsOsb373Q+f+Is/24GQsj+YS+FlCJiUcUP9rAw5NuOK083ff/3dy3PbGF84xWdOP3rjo/cmobwSChIYdS3l9IcXV97cGpW9bXTgKzNB8d4VaDGjsUuqOSfFjC6rupRyL887z4jMzMjMRGZW+gDOUM1EU84++MGg6rpoZt55MwyFY2Q1IyTJYqqCYmahDM77ruuc8469tF0gNtReGXbBs+eUkqmJ9qSNZTpXATPAk09cuvnRddf35i6ZHP//TbT077CNem2PGFPqAMx7750jJDExVCKXU4b+sW/mkHLKBqBZGtEsWcCUzLlExKDgyee+LQmQoMaivHfQvP3hBx9c35s2CX1J7Aj6XKwBoljOWcvJwPEoxu6td158REeT4dpI1jrC4cD96I889OM/fnFexxvXZu+8eeuNd6+8+e7RzY/ag71446MEsPvSq/cuv3H3iWfGZ8+snjk3unBh7eK5ne3TO4O11Wp9Ar4AIAASgGymgqjK2HlfAgSAxObNBgope3jk5/7k8cnXZsd65okvZBtB2HXVsQCYKGlp5I72roZx5WDSkoIL7/6Pv7/+/entLH/v4LXp6mgyO/rzsrF55Xjjh8+KJTVnpAYyLCY3vv2WnVnb+eSTqdtFv9Ld+XCwvlpNthNcB5xDPvOdb4mCfPbjzXf+4JWNn/+bzzz07P/45/6ju3/8nc99+bNy/Q4nyEhpc/TdZ+h7Uoe2weAyW3AuixZVSQBN7NQ0ZiHuG1q0LKsuGRF67wEUALwPBkhRGDCJChogCFmnmQGsrQGRGQ8PD7zzxM4kAzoXQmwaAAhFwY5TSilmFVNVxQyAyOSIoyRE8N6pWddGxx4Qs0XIEXs+g2hSRXaDapByWgr1VVX1hTr9wKA3FiFiVVUA0LuKlsNPkaJwjh2YpZTEFEDZYew6hKVCbmYEPTQJeh8AOu5n7iDQtY1wNjP2FkKx1/Ll966+dfnWrDFm70PJ/WvMjAgRe48WAeWC1l7+g6mvnqjC8OjoRhE6aq+X1bxu1pqmY+erqrp0Yfvpp8/8r+jTbe1v3z2+cuVyXeeVrfE3vvb23n7x/Tfufuv7NwigKmk8xtVVf2Jn8ujJcOrs9uaZjXMXtk6emmysl34UoNwAszdfvqlpZ3On5CChkrKYo9uk1fHWT35yU84qfdzhAmwd5SEiKe1dxTfMtYvdTle3jbTgcLh/T3/l7dH6yfe3Dl/7+oLu88T5u7l57v5cgbRoLXgHqM6R+oN3rl346c8LCke1UXuwe4+2jXzXHJ1bXXluPtehv+vW80df/8bDT//sp37yP73xrT/48ue+evwjn02uKXdO4ltXxPvDGPdKQxuSATqnMS7aOaPr6qb3watZyhlFnGM0SEmB+MEU0gAwLmrvAxN1KQIhERMTqjpfGgj0mNYCQwhdF4vCpc5EJC4WfbgtS+5i1/uGRcQ5h9a/pCHlpKZEKJLZMQC2baqqih1KyoUPmjUEVyD4ULz6yqtFUbh+fN1vk73riZxjZmbuuk77VLL1uKQlzi7n2NRz6g3WSEDUdAtElJSc9/3Hwh79pfaDWkZVTUkQpCqLpmmGKyud6JUbt1+5vHd//wjRO0YmAlDTRMw9oUFU+z5oJCBLJQ1dGJ09+9kod9567Z3PfObkon5vZVWDLwBcl7ooFcyz56L0eWfFbT/7yaZxm1uPffzpo7/x397/N7/2Ry9978qHH+zfuV0f7HVXP1hc+7D5A/FFsVdWOBri+opbW+X19ZXTZ/z/+r/8ube+f3hu54cfPffY8XR6uBuDO+TCWSh9mRy1jl8mt+pLxPwBDE6xthETqNfde/rYCQQOaAe/f2W0nz/8eDj1Uz/251cm/+T/++vtzvrsuG7vzRQKhta3xWKWaoIVzZ3J2WcenR3tesSg2tyut5+99N6H9yhf2D+8/NGdV8dx7UReOfz6m/Sx/2QfjvT9l7ur18rVH1t8dNsPQ7acu27hpMwwodCxxJSzGbmgYhkMALo2ueCw1ylVUNCTc0VAxBjbPt3gfZAs4kEJcooFBIuRAXNKzE7NfEAXAqpUoSAgCqGftndd23cesmMVYHaOqR84iYiYEBNBD1nSmKSf1XVd03YNgnkXiFjFzES1CYOhmbkeO5tyXmblzYqiiCnlLJJFRHp5KKUYl/wxRoAs0mePgJ3jgr1jYmJWESBEoH7EJyrLtwYAgg0r+MznPv/GG28totzaq19/56Obd3ZFq8IXatajb7KasfM93puAgZAMOVgXyzA+nsvWGt68ceeoPvret+79yI9+IbXyd/+HX/rspx594rHVEycrV1RZxwniAvDmuzTbHYzWxLKfpbVqcffHf+qHf+pPfbFr4/7udHZ49NH7+9eu7l+5cXDt6r2jo/Zgb3HlZvvBR1jPph9/ZvwX/sqwdCsnT58px373QMyKrRMvOB7U7dxArF609dyvDZzzx/f2DvHaiY37xSg39aC7e+yRDVyyNv/hrRSqK6HZf/GdT3/q+Q8uX/nm2++UPKB7KQp6dsJ0uLs3Pnv64Ma+2z7Bwb//jVef+txnYg3tweHGhU/keXvj9puhEHI8J7ee5f69g3WTw1/+df9b33v5rbcXf/xH54fF3YN5W9drVbmwmfNBFHOSpmtCUTK6Rdd4H8AMNRKhAZoa2lIMns1moQjD4TDGqGqIaohO1QF6XxhYBjNE8w6Zcxctpfl8YbasvO8fQKo6Ho5FJKbo2MWcVCSbSorOOe+cQyoHg9wIEIiIQxNVBDRD74YqmroYiiKl7DyLadN1BuAAse8FF9XeKj+v66zKgNxHgZm7to0pOscGfQkMMhEQOAdimnMiwLIMjJSRsooZWBYECMRiaj2HW6Dg0aCoouRXLl9/+8phl60qxpQomxhaVkFER4hqbNSyIqZCqkSLRXSPbK3/53/9388+ffjytfleKUMNYRWBYrn2z//N/d/9+uzC1uCJS/65j68/+tS50+dWSjc+uJH2Prp9YrVaXdS1+erc0x/dvnvq8d2yGp86dQLOlY899xBABdBBind363t3prt3jm7dOHjj1fe++LlLBsejyc7OhSfu3HvPbDBZoTbeWVk/4xkliVVuONopBmvg/Hh7Y1Vgb/+1Yrir8307dubGhu3ixnF3+Vbx6FPX9Z7d270ahj/+E19968b1NHM4TwZTgpFkXr+4NeBy93C6ffrM4vBeoWEwWr3zxpu6Ng6TVTu8nUJxcW317Pho/Qze+4N3/VWY/51/kI4OR1P9/E985V8tDg7acl7nWdvEuFgZD1URyarxcHV9fHhw0C0ym7dsAhoCSc7BlyqSNZWFi107WzQuxvFgqJLNrBqMjqfT4BkRvC8lqyMHIFlT3XUGqGYcvIkiYk4x5QxIMcachdmhIRkVoWia1nMYj9Zi7FKKhAhZJWdi6mV5ctyzOgECYvIezATJokiMicllie4HzjpEdA/kSWTqdWsy9GXhoKDoAU1EQYHYeeKURc2IrCdBdF303ofgnbmma4E8AOScTKGfXlNw87b+lX/5a8VoAqKausFgLaXMS64vIBCTU1VRIcdkTgU6A2flAGd/9S//iacft+jbpx5+PODZb77yweHR/Xt3r517Gj7+5Nqbb+WXb7Uv3Zz/yu/n86euP3KWXrj00CPPr1/85InL37/T5kaipHm39qlxMQhdnJNk44iYUFs2h+xOnFg7cXobgAAA4CuQOjM/T6/9z7/4v3vk4Uvzo7yxfrJbzM+eO3Z+yIVf2VkdbG/W86kzm+/edZk1NzkBHUWbHjloOyinb+y5I3tlvLcIJp3u7R7vfGzry5/75Og3/rhuFlTnZuDKRtER+MHh3rXHvvDCB19/abK9bdDdf/vqiaefETNmOxfKlVXnipEd0q3fent9P0IOyVWwwtdmh8cHTC7d3z9GJOfD8WxWhlIEZnemn/j4s08+8cTvfe2bxCzZjMx7Zk8I6gtfkO85oZvlwDFXhT8+PELCHLvC+75gKsUoIqPRkIhjEkQEcmgoWXzw3jnvKS8WRrSyttp13dHxsWMWMBEBsOl8BoQiywxFyhkYxcSx6y+sPfrdOSTCJQSxv3BWVcwRwDla8ud7L2DvMYN+INl1rXO+SymLMBIYEJJRj0axnJWJYxJ2yOx6z3JMio56qzOYQa+KGwISOGJPq37TueLZJx+7szc/WDTMhUnu7wMErKKiAohRE6tHdOa1q+WLnzj3hU+vQN6r2yOU/clO+cxzW4+f/enxoC55+PN//7967bVXvvut9954df+jD+fvX/VvX8XfefHm5q/deP7Jd/7zv/Jjs3l9/uLjRZXHa1Nw7LwDIZBhzp1BEO4MUs7eJPXdUogA2CHhz/zHHzs+Om6bo9TmvXt32qP5O3f+WFOFNub3JqdOP762fna0tnK8aELCO/dudytucl/8YQYwhBhfuVkOhnvj7u6t28NqMg14Z2/lhaef4m+9rW3k6OIoOLZIZdfuLu7cvPyLdvvF7//EP/mLUNeLxeLSpR1tDk6f2L790TyqMBSv/u1fGb7c5MlwMe/CQkoub7WzRRrTvK7bjtmpxaIsPHsiHAxGr7/+xo0b1733xF5NgBRVwBgRYteYGVLf40ei2QIxmWNfVoMsmtIDb5dXJgjBIyiiIPoYU8qp7/VFsJQl5rptm7ZtB4MB4RJRU5RlWZaqoqqI5H2RUmqa1jl2zN7TYDDsYjI10IzMnSgAMlOMMaUIRqbmEOlB04D10FHnnKaYAR05SZkQ2RClbw3soUuZCVyg3nvfZzJVzdQUITbROUcEZsZIBgbOE5HknEFGRZCYN1fLH/3Kp37za9/uYs4gqkLITGSIxCggOUuhZCytb8HKMxuD0WCRodzwp7idzfZfW985XUzWKlqX9tzG+OSPffVHf+yrH0wPXnnrnVvf+96t73//8gfvHt85qG5/bX79g1/7T//s80ft/NKzj3z/11+59uH9nXOTcxfOnz67Nd4RPxwDjEERVAU4gxplJDR0YCF2PBgPVtcIYHz24QyQBIjAujrOZp2kj4j2uuznerTiq7B6vxzuzO/vuqkoujQ/zpePj0ouBvjCI5fe/ODKYoTXrt9bffiRcH5b7txNR6naWE1w5Ni6BR396zftjQ/P/MSjTWy/+bd/89mf+lTJCV3I6K99//pO4v1v/fHixb3xYhwrx5CDSEGUCmq7mKfS5lzX0+GAHBGKadSqGvpRpZaIqO1adhi8I7GUovMFGoTgFXofCzpmj7C+MiHmuu5EJCUti1JBzKCuF9NjAWAXnGgLAI4cMYWiMJNxWZhhirGYTIL3BtC2LTvXxsY5771Pkuu67lIyU0NAR0iWUu66piwGpuo5pCQxyXRep6xi0sbuxPoaA7gsQght24yGw5RtWcEGgAjSc3YA2BBEgJ1nL5rATHJ2znvHhoaOJWXPri+ZK0MlkrFv/WYm4th1/ZVURTKq5JhncX00+aHPPPeHf/xanFvPDFVVYOgNp2yoZGjklRKID/H48FYYT9itluXOqDi1sEUn78fk/fS6Ha0OikfKQTGavPDZL37ss1+ZxfrOhx/dfeXlj959/f53vnP5l37vxgdvvtc2dhS5pEHlmvXxqye3w8OPTB46d+r8I3bmwvbmia3RpCzKgRueAJOUjohELBNIXZtzPoI6S6TemIpqUAxGBgOAFQQ9eXYFgM/mlQW00zvHOvcAYX7jqLs1PVodXt+fnh+NzkzW3jve35jsaJFPfurZ9p/dqe/P1y+uN5YKdrvfufHo3ZVOOn9y9e0/fHP90vnzn34ygx3fmB79yjfcb125+/7d1VOb7cWL6fqCU3J1bjxcPe0PB9TM6jSLo/FQ1KbTuip8wUHUcitYOjNzhFXlnfc5RQLnA+cs1aDKOYVQNE2TRbqYZovFoCoH1aBuGlFoY/bBiySifs7UIlBZhC6mLIqMotI0jfOESprNeZ9z6nLXF4aDAaKr62YyYe/dZrVaLxowZseEjOiaptnfP67KkomKouiP2aKoujQrq3KyujL2QbroRFXBTGQ2PWJ2zJxElzF1xFAUYJZTAutrEHPKiQlFdTJZZXS7+/dLVypi338MiijimTKId15E+rF3T/QsXZHNzLkCOc4Xp9eGTz964tsvXgvFSBUBsmg2JAZPyK230LkisnCs03Fbt4i+opyKYYK1gDgYbQgUoLlrPpjN3zmaebe3VlYnR+Mz1fDiE0/mJ56+BXC8OJZ6AZff+eDKR7ffe+/ujSv3rl2b7t6ZXX+rfvG96Gh/oxqurl4tCnj86fLcuQ2N5aVn8ud/7GmCSy6kLl4fTCYANWJHoKAB0WXhLAURI5bJpk4iFoPdGzdxYO6gy01Gqdsb99fAfmf3Q3jysVtHBw9vjj+6cfi5L3zq4hOnZt95F7SQK4f0mTMEFG1l/5//0d7uve5LFz/xM09dfOLE0K9ff+3l7pdf3f/aZTvAcnOrWlkrN1fKixvN27fGUL2/Vt49NTl++NThvRkbFePSNKJqcIVz3hxVwRfM09nce4/szKxtupyyqfaRHkqZCFMbNWtOBkyAru500UwL53PuALFuO1UN3nlHRVEQgaqWhVezedMp9KhsZ5LRAFF7SBwROsK0jKMRqDpmBzgsS8lKzF1Mi64zdMPRKhOq5kWbRdU7yjmXwZWOm/lszztRdaBKzErmHDP5mJJj7tc/EznvY4zOOettdgBlWSKR5DydzUChKApictbHplREmRwYxJR6i2FPAQcAxy44Vk/TxbzTWLD3mj795GNRR6+89q5zZVp2dIKAISiqAlA2VXW7d+v7u3kwboe2PxqfH1cTxWuSXdRIIAN/0iiJdSnNF9M35sevhjAcDs/4ojJvw9XBcKXaOvX857/yNACB1Ddu7F+/euet1+6+8+at61fv3907vlvr9EZYO3Oay/Vf+oXf+/N/+blPffmpyy/Z/uHRP/mFf/gf/Oy/97nPf4k4uUnrCmFXu4EORpVBo3aHsDJcIYDZzVsbl87P9qZ83HbzLJd3F3vTeGnl6Mbtk89e2N29tyX80mtv3zq4W1y/9yzy4ns32v/402XR3bx6/+p6evIf/bnHvvopD5gX8f1f+t07/58/XLkyX93YaDeqsL5Kw7Z7//aC5Pvp6Oqwbs6c8YxnF4pgg7WyaNLx0WxzdVj40Ha1Cy4wg+qwLBSw62LsshEamOQ4GayYal9aWYai9CFFiZIUwNAUoItN8K5ZNKEoepk9Jw1FmM9mWfPKZJJyLooipdTE6H0llPvMuncu5QSE/ayoCNy2aGKLxdz6JmJEYuhirAZDBOtiB2ZFKJix5zD0QJDc6+iSvOOlKI/eAVpv2gMFcoz9LXIZYUY1VbE+UZ9zNiQARYeiolF7Nl0PbAIDTUqM1g+l/m3DgYigIiAxGhS+iF3TLRbZomgC4T5AqGYAzkyNFEjVFMjv7U0//OD+s889f7Q3nx3v5/XV8UrrXFEUwyT3OzhGCEQlYzWuVkwtte388EN1sLH1UJwVhvuSjrhA9MzMZx8an31o4/Nf+jiA1O3i1vXDDz/64O716c2rBzc+uv2pL134E3/m+YP9tXOPP/HKb/7C5ffTP/j570F7ejgJo8H2iZOnV1bzwf2Ppsd31tYmG5srgHtYH0WgcK3RUSNHEWKezebV1XylGLxx597Zkw8xhAZppZxcef3dcuOT59FPx37wrWvd3YQnqq1x+tm/8197cLvvvnX83auzf/5WeHO+EQb1zkYeD/D0+nAyOvr+nf0Cvr44uvfQqWuLevUoGTWQ2k9fvPTSlQ8yeYcaGEEzAYJqlM4joUFwzOyGA266WBSFoTRN65lFBQGyZERUlbKskkqPz3beB+ebKAB9OF5UJKU4GA67rnGO53VdDUPXdePx2Ds/m3WqxAQ5p66LiH0psoxGVYyxLMvhaNx0qeSgZsjKzrWxZWJTLXwhWbOmnPPYTwAxxti0LQKOB6P5dO4K9kZo9INGDWTCvqwFDXKX+sZmQBJNXadmyz4NREQy7z0RxzaCKD6Y+TjPxqaizEwGpj3mTltNZM6jI+TY5VANa83Xbt1F9sSFpWwmhEhgiH1dbTYUVbd9+lQxWRmtluPVc8fT+9PZO02bnC/OXbpw/a392f7hI5fOiYuuoC7W7Eo/GlRuZzQ5mdKsaW56zE7EGumI2EOHSj4glc6vDMLqo5dWHr30eC+vpXw8r6drk6df/vqiPF21ja1sDCG2p86fOHXqjPfj06d36tmNMVz68N4Hfl9zM/U8s6NWIEyO0/7Ll6uZaefild3mYDZ86lK6fTwcVCGm0caKlNxcvl9hlXHm2dG147v/6A8f/W9+1DaL+y+9t/+L36wvz2ARyqsdbq5HieOLO37Rdvf3r7f1K6er3WptMeDtyCvj9Wh6Mx0OnH/vo2u7XT3h4DyBglhG4ulsMRoOymHVtp2aMhFAJo3NvEF2hNjjhk1zE7sQgmhiAVF1jn0IpXdd3U5WBj30n0vHSHXdmKbhcLBY1FVVzRcL6GP1AKurq13XSc5mUBQ+piSS1WDWJgC0nDEnRy5LylkQIUsK3gOYd1wV5fx4EYYhVKHpGrds6WBEOqrrDOao6AMbItlCKNCZSkaTvnC+GBaz2RyMsnRFUUjuh/JLAA4tzWW9Ec9550xVRHtQLzyIGtkDog6zAwFcLnGBDGXhn3vi4d//9ivRsoE6RFVLhGxG6sQoSM6hHRysxHfLo9N3Nk6PN7fPQFfbMRxND+7v3vzVX37rte+98/RzZ7761U819Z2HH15f3excUY/LkwgtlHE0fgThWDWTYkhRREQ1xw6skW6RUYA80xiwMC5cyaPRmpk//dj67p0Pn3j646PJ753dPpk6bWcqvH/53pXSRoe33+/irSee/9HDW2+Vk2522IyKjkcDObqb92pPo/b3rrX3pjt/+nN/7cxn3/raH7XNwo3WPKX1lfUBLHREkV1Vjpv/6ZXLe7fs2A5fulosYFxN4siLJ14ZljTML17V1ZWXVu1NrQ8HnhLIUTcXQUAFnHBRxxTRr/oRqqQsDcaiKBlxdbxiKnUX2XNO0TmWlIdV0bRtEgWALsUQCgQsnPfslIOaJkmGmHLMbWqajgI55mFVDYrSzBaLus25EwuhLMsipcyEOatFSRjFxBceVQhgVAxikdqUWwUCElUVzSBE1K+FalD29VdgFlPjPZw7ceLazZvDokgiR0fHm5vb9aIeD4dd07qUkxkUoSqLKsZOJANa6YumaZAoi7oQiHhzZev48DhUPSLUnHMimYhVgcDY+ZyTth0ROu9iTDll733vV80PGr0sZ8cOEAQNGSVlNH38zMbRExe++9pHIfQjEDAzRem9RQhQQXsxVfDa7pvf2L19tlj56rlzX9jcObM92p7c3m9ff+fWUb3+4svHH77/7Xp68Mgjm089vb59wj39wrkLzz3qcIHYEuUsLL4gz0jgEJwBiaH0nrOFSLYkmI+7VgCD0P5qtVad5XNnh3/rb/+lM2cfYb/WdNwdzvW42z84lI1JW+OHMxtfeHhv/rLf3jrocntlPvjYuWO8utH52R/c8p3dfeWdb7367qmtsRCMh5ux3UPByVqJR+hdQE4+B/n+vXyzGxaTAdFCWn9iy2Y1XL7flv7yRvXiRrzlbKVcqwTbRZ0BqSxAhZGCiDFgQdx0KeUQgjlSk9jFcTkk5xWzZkUzzZajCAOgA5KiKEIRCGm+mFdllVOO2ZwjRopdLMvKFL0v2tiBs0YWHvD+3v1iMCzKQrLmJNNumnMsR2OVFFNsU/RlgWYE6BwvFk0IhTMMWXJO3jtkjimGwiNAT/UyNcmZnUNEKvDevV1GBIOcxYUQc0ySZp0VVeFMQdVi7BBjH8ZgojaKgVMFk549Gxw6EWEm1WWYtm+C66szskTr2TXe95xcNRFFVc2CvR8q50wMzlMfBuy99GCG6fizT12qF+m9a/eAAqgVaJY1oiloA7JNxSjbFmZqbPjKUfe9+2+cca8+d+rMVy5sfuKiAilgNLxxf+qgeuv9+vW3b33+h564+FR6++U/Hpa2sbpaTqCoCAODH6hmTcmIlEgB0JPDEZkQgmmCHEENDbvFwmIW3d1cmcbp3cJPRjQarq7RtvfBA6yAPSGciDnCs4NqY//9Ozf/zj++tPnlZjCqF9kEhja8/7vvfWN+9c/8b/+zMvi1YrjvjxZt89HNu2vRn2/NqS5QJic3j094evc+FZ4v7Sze+GjoBte28f3t0bsTnIFMZsqzdFhCMS5dGzVHIkJUdKSibd0gQlmFvs/Ah1CWoZkvPPPqZNw0rRDnlMm5lCWl1EpWwBBCTGm+qNVgMBhUgYipazvnlmjssvJGZiqLtokphbI6PDpaWVsvQzk9Om67enVjrUuxzYmdc6bLc5dosWhT0sP5kWStynJ1baVpG0IMvmTnujYWVBxPZ865HqlnYEjkiY19FxMxj8fjRVOHKnRdzqoOkYnUQFPKfZNNv76YWUVUNQQPJvfv3yuKovc3/dvhExgiFuxVoi+8ogGh5+CLYJJVNcbYmwkAsSgKsyyiPToiZa1C0cVOoyJOv/DCpaatr949JixQjdB5QzNIqCMOoSi7Zr4ikcdc0KZfRP/be3t/+K7/b34KQuhysoI6wC6jcWmw8u3vfPSln3zB0f57VxY3P7h25uTKJz//zEfXX6HKzj10+tz5E+IUKuc9A1huB8CkLJmChoqQDIQHbogMkFdtPedGMpgwpl3NqctD4uD9wjs0OhojZZZird0+vXbvlesFjJ3LlcRARTdvzz/88GhjPcWu0IqD9+Py4tmHWVxeuSoHx5wRw7CI2T20tWgbuj9lgWub7usjvZsaPuJBWcIw5JTXjCMIlQHFquFgPpszc+wZm2C5d5c7JzkbgCFMF/NQeCPsughAoICIRVEyKiGpiKmur6/VdZ1iDEWRYhoMBiIyny/KskipQWBDKgdDMIs5D4bjpmln03lZlmS+bds2ds6FnPOgLFOMMcfD4yk7XllZRSJiqJumrIqcU103GxsbdR27Npq2zMzO9TG1lLJzXHctGHjnRCTm2NbdwppBUXgzV5YlAiSJZVWAARgRcdYsIl6dmQDYymSIlhWh72S3BzXXWbJzLJoGw2JlZVR3TYzJFE3BFYUBeO97v0x/wquqqSGQgJCjwbA0kEiBoKuoe+Gph27vvayImlhdQoNSYWF5naqdybhqpl4BkhDOI3aDYTUcr97ck5u73aQYR6vBUZSMuXNoi/lsc4K3dqe//htv1YdocvTLv/GhGocgo+HLX/zSJw4Prm9vj06d2tze2T7x1KVy7FSF0BgBNBp0AGTAqk6Qjcue/4tckg0DvmAwFLiJeUPyjYx7CJsaZwubwJvXfDsX2o5F1WEzeOLcn/7f/NzNux+dPXkqHbZlNTh1/kR3MF179NLkY5f2X31v6CduvJZv3oOXbuCk0sq/s1L8EdVJVzxZTl3TRlUZhTLnvDFZu7t7xzlSKcqiKMqqSzHG2ENBRHpqOyoCKFbD8axLKcaiLHJMznEIoe06QgqBxaAoAgCUkxVmrrs2ptzXsxBhzhFRU1ZV6Ctl+rDbYDAwwMPjI+cYCKtqkLpkZnXdABKgy4JqmlMajwcm1iU9ODr03iHT/sE0dhZCAEqmRkoixswm2YEHUHYOAVOX2bm1lfV5XZeDUNcLR0ze+dyISQ9wSkhiP3jYoMUu3r573zED5B6zqL1fyYwJY1ubmkGo624+r9uUTM07h2rs2H7Q/wYACIqIjAqScyLio+mMkAdhYKAZmpNb66cmazf3GgqFZSsEqOSqcivg7l++fD4mqELKQqIFFxZzzmU12dB8pDbWgnmGQ24iz4/n9Oylk+cunZx1+1m1HKzWdXsUYxIexZWmTb/4T/8IyReBsL3y2S9s/Bf/97NirJoFhYAYiRENtM/pGwgoEKKIQ40Iq0BbZgS4hmwFfzw3u+yvRGhhcX9lBrUUpDVrFyNv/cgPXfrZn/x7/+f/budTn72RP2hvzstANdCgXmiX5ewaNPnwoxvSdcXQJ8O3C/juxKW19aB+UHIX29m0ZlVm5BC62FRlZYTzeV0URV3XMUcAM2QfHGWNMRehzDn50iFBjF0oi6IIo/WNe/fuimUQA085S5aM7Bfzufe+GhTEhlm7dsHOec99Yn3sSEQUsEsJDGNMoBAlVVXpERDAMQ/Gvm0b4pCNmrpeGVdi2sVu3tTj4cgyDKph8ByKYrGoEeJoWDD5NnaOKXbJsQeieV3XsWvqw4cfulDXzXw2XVldRU2LhQEHaub17HgKYimmpmkUQUxjjDmnpmnbOtrSOGypS9LbRtV6t6ipZsnsXEp5NpsDYOFDz4QRlbppkuSmbeumjim2D/7p0eg5C5Ejx4s4rZucas9Zv/i557bXQ46NY2hKyF1Ote6SiEkutXNIicS5BaOh+aY9NSo//+PPx3E7cByaopBB6YrV4B6/sLayygyl6gi4MEoEwBBm7dwYi9Ewo0tUTDvJZG4wMAWO5jvlHCUt2jyThJZF7RjoAPDYoEPA2DVACUgIB2xrYPOor5i7rriQel/rYxUhCs6ok4htWx413/mXv15sjbvjYwa9cfv26sq2kF2/9/7wJ58o/vKXf/2svvfU+dHGifd2yq9fqr69Cfe6zmbRFh3EVHl/7uTO2mTUzI5AU5tbF2htZbS6OkIyJPOOx+OxY9d0XZSMjgwtaSJHXUxq1MW8qNvb9+51WaJkQ0ipy5L6pr5Qlv8/nv7k19Z1y8+Exhhv+RWzWMVeuzjVreJGHY6wA4cLnBE2yrQlqoRUglJAIqFEWIAgkx4NUgI6iKQDDWjQogM06CVgJWkkg9N2hEmHHXYUN25xzj33FHvvtfdaaxZf8VZjDBrzhP+DObXm+r73HeP3ex4FWNYCQF03kLFIFxOaXZZac7ZkEDHnbL3bbDeXQaklE0LXREtjVkxNpzVN59MwdDGG7ThEF25311LYO2fJIMA8T84b50xryV7WlUQ+OJbWdTGXfH11tdte3d+/T7n40H31+o318Xg8zqfJ1lIQkYnIGmO/Sbt5Y1SVrLHGXu7gwVlGACJvrYjmnC/o+O12m3MxYFS11aoAylz+TJXJjS83JFWNMa4lj+NQa12XFZGqVGywyOpat/U7aGn05Td//du/989+eno4mdABOFnlB7z843H4aO52reQgBLgtYNGhwpf/wX/4r//Gt3/nX/1z5+fbv/N3/yDNfDryfC/L06ywfPmzr0pD19HuZpgmWpdh1Xaa1y4457wPlrb64uPnxg3GdZmTSiNuhpkKA4iicCOkDoSImjUPwYHCu8R/DwgJJqONIIhaAfT3J00CQMBVxZFiMzL84PPfSz/9rf/Ov/n1H//IEh7m47M0uYWmw+F//R//X3787ovnu7vjw/svTf1yIwcsDc2z2xfTaYo3W0J2ivvN5qMPX715++Y8TbW1ly9vT8cnY6CLdprXEDsR6brOOCeizMxcnXPOWQB6OJw2m01alxCCD7FxQUSrBkCZNefUmhARKk5LtpZLaV0XWJFVjHWtVoCmxljjSymtctd11lBJ6elwEAVA1FTXXG5vrsuyMMtF9Tt2PahiDKkWlkoIsesAwDlniXa70SzmeDx1XW+tsEgIPqfqnK2lWHKNBY2blzwOIxJaJSRjSqnR2eh9zhkv2AKiLna5VlH1/7I8qioipVQiaq05a5Z1BSDm5p2zznFjZ60C5MuPlZDIGAQWKa2y8DwvtTYiAwC1FgBANEiy1kNrZVrn2+3+b/3VX/vpD7/6xz/74oD5GjqQ8H+fzj+O3X8O7F/g+AGbDOvJMzHFt9z+wx/G3efu++N/+5e/q79y9SfLm9//T/70N3/7F9DdTkt998Wjbr2PekpTA3DWGRBP1sVeMbPWT7770f1nnwqF4XrXbQZwFkAQqgUBqK0Qwg7FlPp+qV85MsRqwiJAJAbVKQhybmLyZ482WyYAYDXGsaih969f/7n/6r9WzlM7zV88vR/2u49+7Xt/+A//8Mc/fDNlc3v3cw746e39F9iG0O27oQoIwYuX164KkJnz+u5HP/wbf+N3Uk6llK7rvvutb91c/8Y/+t3fe/90iDGcp/M4bpZpFQUf3DAMqpJz4dJKLddXu9ba1dU+rUvXdWT68/nMLN/IBEUuZMzLKJCIui7WmkX0Qvs2xqTL7t7ZtCZRFebWWknZWaeEimCMMYRlXWqrClprddZOyxy7vgqL8mazaVxBVfibwPHT08nHEMIF4QTLsnZd//44OedD11tDMYbydap5Quu7vrdAJAjW2dYal3rpZDprFNE5t+R8ARqINJELBpkuVXwFMYqGDBKpUq01hqhGuV7ssBfh4kUmAYiYcgGQi2+RWUTYOasqxBZsaViZKHZ7zTJ25ed+6eXw7asv/vjLL94+HXK9WuhLlv8jTn9X8V8J3V8y9paLIqcI1FGgxn/0Bv/TJ77D3/hLL//if+bXP/zlXxb86Lf++i+ocW8/f9P52x/9RH7wgwdeTS0L5NL1aAOqFpPWL/+nf39+d477rnu+MZ/s6eV2/OAqfryJdz0Fh1EiDDHuWy3UqgKJNpJIF0MPNjTO0fD4x19QJfCkBEwMIpjFff/FR5989P/8B//wV64+/qPPPv3Fn//+P/iP/sEQOhOLt1k1r80YCJvtcLXdYRGzrhZIWp05czWGrCL++NPPpvMppdUa/wd/8Ic311eG3F/49b+wu7r6//z9/2TOawz9+TgbxAzgg9uMQynVGHLRsZhlnnNKwTvv+hfPXhzPy+PjYxeDddZagwC55PO8DGOf1nxJTg59xyK5MlnDjRXaOI7eOmntAghOtYhI7C7zwDaMmxpCLSXG2JiLsNSMCsPQq2oModUSfGwilSsXntO62WzTkkptzvnzNOfW1lKvNsMwDDktH3348nh4LGptCPgf/Hv/TeOsqJRWhcUgWWPEtNaEwNSLB5sQUS7XcyRkEWb23l+A29LYWltquVgziag2VjV/hukgIrx4yIJBFhGClNOl3koANnoWuUAQEam1RmhU6gWhkIV++NOvf/Kz+8dzjhrESMX6MdBvqvut2H2XKHo9tOIkGDClzKY16cL1v/qXzIt+A0V/fkPX8MHPf3Q06TAd/vRPvmi8+fEPvvjih+/ryWt9+7f/nd+2/4ef4ZGtADZs2jI2cYij1OtNvwv1Q7P9xW/d/fW/nP2pt6nbvFCMln5jMn/St0NeMk/19KP3P/vf/j9efRW0uBVZ8ooIXNvwm9//4W99aDL9R//0n3XjeHNz84c/+snQhZLmltcqYqxHRKsw9gMh1tqenh6NtS9evpjnMysrwprzhXPiyKC3LByAOt/lWtZa5lpe3b2YHp6yKCOx8NVupy0bMrmVZU39MOSc+xgtIBrbrJunKVgDLDnn7WbXmN8fjgJAhoL3feyk1mWe5tqMs2M35JzHYZDaaq1AxMqd98qttiqKxvklZ0XsjQPVpeQqbNDEaEMwZS2kJsZuWpbMlZwxF6GriIg2FVGZlqVUez6f97tt38fobd/HtMxLrsEH/F/9T/4b/I2PVQwac1lNEuRSvPMXJyyAAihcwk211FJV1ZlvBIpIF10ippQQERSY5fISV1XvfSl5mubgvTcoF388EQIhIAF5b0vJxlpWLqU0bqJgAbS1y6BKgArgm7fv/vmXb9+9PZlZpI/NwjYtvwrxd168/IUlb7QcKLsFeupNh+PNTTry9pzzoOJVOoKP97tPXtDH2/7b+/yMnzDNk1xtXKfT+//e7+3srgh7679pvCBUAmFnW/PLaf21797+u//WZ/+b//0WGoy74W/+9rPf+S9+9n/637mHp3qqus7wOPtMOEvLDCy2se+6KS3hl75V/p1/7f/8f/2//eT1u9/+7d/+2aefHnPWVtd5BoDNOLCCMQQ1qwgZe2kxhBCcczmlWluIYc3JOjf2vbNOQSrXlPK8rt4Ha713oeZcywrWned1XtKzm+u8pL7vyNE0zQKw3WxA1QIua1KyMTgVXpbVOrem5Fzool/WpbLknL2P3lhnjRizpBR9OJ/PCLDdboPz03RWVOcMGVtrM8adpjm3EqLdhiF4xwDrujpjvSHRtqYE5Gpr1lqDVHPpu15EUkrGu5RzqYW8ywVqKYiAoH0XrCEiWpYl+GCVQFTRGWqXerHUUkPsvQkgKsrcmqgYQnRummdVMRcpHIohczGMxBhLLmQMKCiqQWq1Aqi1dp7PrdUQHBE0YeOcitRax35zKXGej+fgnaKcj0cB3ey2OWdFa2IEkcfH9+M4BE+vbvpvvfrVx3P+gx9+9uXbI69tBf/7wP/ki08/tPavu/E3w7jZOl6m1ipy2LlhuNkUK1B4nIj/+bz+7p+wofcu43UHt1fdqyv/3et354MtBoQBNXGy1hk02sQbAbQlOCzx6td/TW/uuq/xqtvOT+367lcPj2/Hf/pmhHjKFktvJ3/OJy41EolIsbi2ZFloKfev7//w80//2l/5G4fpNK/z0PXvj8dLf4FZrTFcWgw+58zcLs4XZs45G6LNZiytbjabWmtjds5H6wlBO1Lncy5WkUtDVXJeVGMMzvlauTSh0pbjXLn1w7CkTACo0Fg4ZxANwSHQ8XTux01lKYenEKM1pD5YZ32ILVeWts7LdDpf6F/OWlaJfVSV4+k8r2vw3ljnnQcSabVguiA/Y/TYGFkA0TgnhN55b6yU5kJstaacGVQaishuv6+tWa449LVk62wpmXwI1ooPVdhKY2uM8b5R5dpUUQDWUgBAga1FMMiNRVHX5qxn0W7o1nVl0dLqBbK1TLOqXpKjMYScy+WoGmM0xk3T7JyvtS7Lalzr+67rYq4ZWKWxi1EQjqdj13WWMC/zdrsVQiT7+P5JwKAJCspSztMhevdX/+L316Y//uyrP/qTz3Ii7N0Pwfx05f9Qjn/xuvsv3G6+lfqrTz5++vJzd1obo6kqQhLJdBuPscdJZ6OPBX700+Pf+SfRDibGyqoqCKrSxBKgAZLisUkTymYfp//X791cj4/Laf/x8+4Xf2n+7P+ntWWQlte8HHWFiEhqWKAhCoglImfp7bE/pV/99T8/qvvjt1+hpZLmEF3Oret6BCi5gHAzUFuLMSKggsauH4b+4f3jmltTzVIaM5Z2OE6bYUjr6qP3IaLVlJJ3JuXGgix1s9m01kRh319N59l3/bbrTudTysvlIcpSl9aKcGWutTbR0zSnnKMPx8fTuB0ByTq/rmk6nvfXe++9ihhrN+M4LQt8401tABRsiCESgkV23vq4dUCF25JTEx2sz7Vk1S54ESYCrTwvq3cBEIbtprZ2cdloY0nlkgelb6xLKWkVQTZWwVgEiM6X2kSEWyVDtnfQkJmtNdaSIjhnjLEtNxGxhoS/8SQJwmXHT0DWWYNGhJ11RCbl/M1dStV7p6JEph83zlog4lalVQPUdUGtE5HQdbW1cdzO6yyiwhWxGZC+j8JVRbsQc2m1CZ+nLZm//K2PPhi2f/z11z/+4jCsYOD4ueqnP1v+cV/+fD//5a/Kn98/Yw9Da0FQcytW01raMpkhF1IdnMHq/cby2HQ2ol4REIVBpKB1nNR424Ft5OX3f0I/fZfSRNa6Zy/ogxv53XMyXVmbzJnWNRgrhRsrBKtINothJWN4Wq8lvNhff/npl8ZhVj4/PMZ+iDHmXC5aX7KGyJa2WkFRLTkPYJ0TEbOW7IJzxrMwqORW6ulcSr52PgC2Wvs+MjcbglYFQWZhkcaMxgkIqC7zNA7jsizzvFyu7c6HtK5LTtZactZa62LoY39/f7+sue+6d/fvtpsNEV6uH33XHw5PlshYezwevQ8XHMdmuzGEFqHllFLjlpohRe1CAEBAqqLWh+BjWmZlIWtdjOSc1IaGLhiJGHtlVoGVm4heSNnDft8aozVpyiJgY4wi4oiI2XtftVpvjDOGonO21m+w88ysBoBQmuS0Bu+ZGzStwjZ4VlFuZMhaU1rz3geQC378Es+7LJQG51trXexMN9y/uweCw+lkre36nhDBmOP5HIfhOE0gNvYxjJv18Jhru9rvg7E2dIfjeclSDRTI19f9X9599MufvPz64fx0mjbnlHM7puU/TtPf+frNtfn8hTPfie5bY/zWbvOy626fdaN4eVxuEkJVxq4ZTZobeK5sLx/RoNQ8ABS0uCTto7o+/5M/YYc49vvnH9RXH1KY+Kfv3Qq4LrKkZnyWcvWtDx6++hrX4sABePa2mUaD99M8TcejKX6up+Nj6Mecqyohgo++qrx59y6IXt/eKEHNrQqnWtqx1iREaC88f1YW2W+HVIqPvhHcn46OzGCj5BVVBMU6p0iAZs35vDzd3d0OnX+4f9/SQgq+i0/H4/vD08v9NQEYF8AYATaGrCFt6+1+ZAFrjMSgquTs64d3z25vz/N5v78ChForGpqXlRA3u/F0PjhjRXW/vy75GAkFRbh4i9533nesNM3nc62lFBZ2wTeV4L5xtamIClwcI+SDLYyORGpesrLG2HEriA1AbOWKSAZJiaxBAwZRQzdw44uoDvQbhBgIggAA9l1HZGqpCCggtWYig6gXRREAlJIvmBKRbwR2zMytsQIzT+czGuO8z6WkWrchpHUV1RCC96610vW9x2/A4cPQ11LXVOzoc62ifAkPNBUDUIWuxvBy12W5m3IVtAqQU57n5ek4PZ3nv7fk5TTru/ut6pV3t13/iu23svlY/UdDf+XtEOz1OEquaV6NNRW1WDqW6hUtuLIU5hZjsKqBSbn20tZ//sP1yy9NXjQlvHwUounxuL+6eUr3qoKWOkIo1dztF6s/+/xnDYNHt++vJqmFGzXajKMKS60vbm4uxzLnPSh0XY8X+ng0gAIgLDXXZJ011noIKZVaizUmLfkECyHW1hSkNL5o4JRlv90F4/KSx3F7Pp+v9uPxPN/ur4BZkUFbDF1K6/l0js+esaqxlrVZawAxxphScs7td1sidNH/yY9+8OzZs2EYfRd9gOj94fDUD71BWpZlXWbnTOUsgpaoVE5pNrbV1sBArkVBQoiIFLy3xuacVVEERKTWtZTcdT1e8OFA1tlpmgAAUMdonbMX8bc2baW2aUrOmu98+5P794fT+eycFVFlFpHtZtOIBEQFRTSlRVSJ6GIlY+bgPSDWWgGg1WYvnB1RYwwCqULNWa0hIiTTRC+RluurKxUBY/b7/eFwqLVebgm1VCJKa0ID1jlUbI25sjIYR4BoyOe0MtfEpiIhglXcDMGSun1o3CE9RzbLyk/LunBpnF+/vX+9mK8a/7/rE7ZmH+oN2mc2fieEl+RfNPutbvNqv+3RWBvQoRKm2oIxqJoa1zntnK6nx/a7/wzevS/rFJhX4Q7IN8Vaq/Dzl8+meZnmNJFOO6dX5p/90e+vtaFx75ZjP3bK2HVd9P5wPFpHxtpcWow9Nq61MsvheLy5vWURY60wE5I0brUNQ19aJTTPnz87n6Zpmqwxxth1TV0frbaUKyBO8xK8j9ZMxyOg8d6TsV+9fv3xx5/M8/T97313zcs6J2UwXb/OS3B+XqZlmsZxHMfxfD5fPEnGkKvy8d3dw9PhF773/dPpiCyB7PWzm9P5tBlH70OpNcQOiYK3RMi1GTQquqRZpMQYrLPgMaXUWFR5XbOwKAISrctyGQ1ttzskaszSGhHGGC93MusMCQCAvRrHp+OpNBYVMKaK/PTzLyoLIJZSVfRiCVrXhIggKk2KVGOtt55FGsvFC7Gm+k0iRMRZd4mAqmpjuKzmnbe5FuccIqACM5PCBfOkCg8PDxeXTKkFFABNbs2FSAYulj4CRhZPlsCmvAJAH2NoXknVmlYKKoFCYyWiw2FxIQzOEZSbDYWMMVx9sN16iwbcsvLjkufKj4fD45x+vPA6T9RqXF/fPOInFF5Q/BC7Z/1wZe2LGHsrcethG6wt5uEhIt1UPntt0mAfpyIHbWvgJ1OPupS9fxOLHfpq6WePP2ZnOxqxtpcv7x6O74e4yWmtpXlnC1cmRcLD6URI0tp+t7PWLPOihPN8ZC5//jd+9fNPP1fWvJbQ2SUt1hJz3W76dS3n6RT7odQk0mK3KZWdU0sktZZ1LUCmFEE1zq15KXVl7nbbq5Yf5nV+/vwueldqslat6bqu//LLr7oueh+MIe/s7u7m8eF9yiX2/c7sL432UlIp2fuQc7mUJoyxIrqu+VI9R0IyZJ1REE7YVFLKgskQGWORoNSqqta7yzhSgQ5PJ0TabMZSs7XRWFzXdbD9nGtK2b57esq5hBBZ2FuLgK1WBSIkUb7AJRAx5XS55rI0Y03LNWsBIBURUGnCyiJqjSUwjVm0IKoxVqoCEoFabwpqFUlL8s5fWvJEkVDJ0AUI3ZhREJEas/OxclVBEjVkVNUYp9pQtPMRUB2ZrC14X0omhaWsztsY41Iqg8UCySD6qAhUIZg+rctUkjNsHFxv5UVw33lxK00YXS2t1jblujT5ybuHP24cMLf56Tr2z4LfG/xgu3/+4npP9eGLH54/+xeDjevQnmAKt/aY8mPNaKgokzFoIO63ZSnH+/fD7e5m3E1P5xBC7Pr6XlQXH31KqTJ7H3PJ3/+5737x1eu0rC7E87w67w6n0/WzG2+NYPj6yzfGWN8ZcqaJcJPnz19+8cUXzgcRWPNKpIqmcqktOedViQBzYzXWGouG0rL4EN69e/+dj7/19PD+J599+eLZ3e3t7ePjIyBsd/tcCymK6GYY0WAIdl6X0kytFQDR2lxba2qMrY1hSYR4PJ77fqilMjfnbMlNGJhVtLVWDFE/uuU8b+KmFAayqiIAV7txXWdFiKEXxs1md3//cH//KADb0c/TQVQNQj8MbOzT+ydjrAG0jGi9V9CckneBFFCRyHATBRXRxgUBjSGDVgSIjDRGa1BARFQkdoG5IhI3FmFjCRikio/GGiJ0y5KMgQZccnUuWDJDP9RWSlEACc42FkBVhFq4ptzHwRDWmli1NSZWE8KcVwFyxgpwrTWEUITR2XWploJwcT7W1tr5FL3v+1CZrXedC7WWChmI4zZ4Caw1dna3GZ8eD9KgiEipBsD3YbfpCOgXXtwgoI32OE3GeyBzPp6+Rro/TIfpbeijCSBaRQy7rdQcgnXWGqKNc/fv31mrQNmoXN3djMO4lJKw1bbkr77sQ4f4Z0QgQEMmutgKW4N93wFSLrW0NmxGBI0h1FzXJXed55aRsQthfUw/+fSnACAyE9FuuxUWQHTWE0JaJ1HgBmQsAxqAeZpiF4W5j+Myr5WlH/slr9aaeV2sc6FyyoVUnO024+Y8nxSac1bVENmcUmUm0uDDBXcIZIP12bAi+uiFaU2JgICl1brZb3KGEDwCdl1XWgldKEtepvVmv03zGoNHgJoLonl3fw9oFGFNabvxse9qaylnVQghbDfb++NhzRn/l//j/xoRGTLMQheTV5NS28VtYAxesE2ISGgQkIWNNa3VlNLFkh372GoFQu+9sKxLcuSI0FggwvNpASXnTewcAs3TgmSUEUi5VWMNGsPfLEgLgjKLqlrjWIUBDCJWDj6sNbvgL6L5y+O6loaEnQ81lwa83++XZVaV7bhJaUVjrDEWKXYx52yQWm1AmPJiiBQEFKx1p9OsZBG0cbFkLpO5VlktgrCUNVjnjDst5XhOofddZ7rgaylEARQzF2WxxuZS+r4/nacQogACQO8dWpqWBQm9DwTIpYEYBSjaYhdKKxeDavAOkWph61wTNoSNqzJdgu4pJSKky3bFUi2FG3vvEOCyzAOkdU2ppLvnzx6fnhDIh7gsK6cqqLHvrLOAcD6dQvSAwBc/J0AMAQhFxJuQc/POh2iH0Z+PJ0LTGEqtiNBaA1DvfalZG1hy5G2prXK11rZWLRIpNhUAJGNSytYYRACEytxYcyrbzVjWdRy607w474W1lKoIxtpSynm+jP1N8O7y1XJONdfNuCMEBIFLuDPnvKZSWhOVnJOqXBRbRESIl68lCqU0VbjgyS47pEsiWUTWZRXRUoohstbknJ2zxprr66tXr141FSUstRauoqBITaS0WmopJQuztW7cbGpjFkWyLMosxrhLHbo2RiQWPZ5OLOKDJ2OVwAbbd13K6eLXmddV9OLTxNZaLZUbz8tSSjlPp1oZkYZxp2jROmNtKrUwA5JxDgCcd2SNIdP3A5EPsZ9rm3NZVUpTb3sA7LuhNValPvQhRlYYxi0LWOtrYzJmPk/n82zQjsPGWldyUWHFb8hEArrm3Fhqa+O4QUBuQkQietEFqnBTybXmWsZxDCFasgatJRdjH2KHZFNuS67Tmk7TstaGxubchKWLMa+rRRy3m8122/f90PcqOm62oeukat+Pm8122GwVSZoqKxER4WeffRpCuL6+WdZVFIAQCQHQOSsi1pqXL1+knNdS1nUF5Kv9FqT9uV/55ejDmhKLXiJsxjoFA2C8d4QYvQMVAIz9IGhEEYCaiO+C9Q5BVXi/21lnrLlYQ8lY65zbjhuVhv+L/+G/ebGvXOZK+o3pQud59t4RXTIfZK1FMqrYWhMWQyjaRKtBc/E1Vm7MEnzgqgbIWjUOW60ARpUQVaGlVgFwHLbLkk6ns/depIHVse/zkvpumOZ12GzmdeHMikDeamlGMMSghlqr1hhVZm6XMkm9nLitbY2FAACi89z4Au1TUWl8CZVddsFosdamCqJamWPsAOHh/fvNOBhjlmUR0dN5vr25A07b3faHf/rZcZo//uQTVV7TxJlurq9yPhhjQhhy5n4zHI9HYfbeS2t5XQyZ/dV+mZbjcYpdp6iXZwMhlFYb4vF8GsbxoqlU1pLSOPSqYK1nFudtE2YuIhdeBkcXuLUQorBeOretNUC00R8OByISMIas82gQorPBeouYSitELWdjyBubW3XBiTKvQtaw6kXLiYiejGqpTQGwtJLrcvf85fEwNWmq4n1wZFJK3jlv7XnNa60xWOFqCIfYlXktrFOuiCgEpZTgAwKSajCoiLkUIFOqMCiz9s6kkk3wCpCXtN9shbkhizBe/JKKqtBaI+8UwSqosrAIoSEkFgaBMDiAQVVbE0Sy1jYWUv1mh4TC3wBBjVrw0YkwczPWWTTGaG2tKdYixjgUICLhlksTdM6Z4/FUSun6iIDMiKgtM5FlUeesqLTWRJp1jgDFEiLYgCotRtca51IBtXEFRBusiHITY8gjIKIz1G26w9OpITlrWbm3djNuTscDGyUyV/vdfD6n2kAFlLlyDGFNRVgat7HvvvOtj1pOc/XnpTEiOY/WRBs2Lj7g+d3xadt13pndZjjqdD6ekMw8nVOuYz/EfgPSRPj25orEvHl/6DejgBjhTR+vx5vjdOpcgIYiYtDkMm/7ngVzKefzMmzGdZ5rK9vt4BBZFMGvawIAqdk7s+SFRYLzLZVSCwAY57wlbyyKXqSjtfIFk8y1sDQgs9RaG1e+lHbFgSuVL501FW6G6GLWQlRQa8Ljw1PwUaSO/QACjbm2pnDx3GPsh8IVjWNu05K1clP13ohoWYtz/uKcvmCac66pVu+p1ASIwix26OJwYco640qprTUgbLX6YBGUAYm8qCIrEVhRbczMbC2UWpWl7/uSVgRgVma2ximIaENBVRHBy9rdORdCJMKUM4g4F1RVhK01uTVVe8GM+C68u7/vQiiNfQgASIgxOu8dsyJAbVWRWmMiVtX5fCZLgWJwvqmqNRfGOaLmmoWBLx0oa+iSpUIC0stDnbnF6J21Ik2BmqozpjK3deGL047lfJpQQbj5EKZpcsZekFIxdk4ai0ynYyvl3Wldl/T85vbVB8PD0+Hrp8MHt8+vr67fvHt3PJyffeejdZkR+P39+7vnzz558YwZamnMejyfW8n77diPpl+MIn/y7U9U62c/+RSRbnZbaVxYLtHJY66IVBtfXd/EGL/++uvdblsrGiBLYIBYJHqPRADggw+NL6cXdlBq8c4HH1TYEEqTNeWu66yzb57edbFzNpScjGJrTRUyNxUxXqQpInnnUlpzzrELBsla60PIOdfKfRetNQJxTRVYmkhtUuqq3KbTue+HEIKqOLI5Z7ImODvNM5Ft3Jz10hgJnSW8mEPQLEsWuayDzOk0hRBijM44YVEF53zhxqq1St91Ksgi67zcvbhalrNlELDkrWmtdUMMLhyPp85j38fHp3PXRSRsrYVvXpSulEJEznlEKqVcFDhggAjWtHQhqlRjiRFTreV89t6Pm02tNXaRCBAVHRnjmQW0WYNIPqUUvK+1gmrf97VVrGrJAKsw5JpM9LWVJrIsS98PjkhBLZlSClmXWByiklHmeZpPpxMZ6w3lnE2Ise8fHp+cc9xkPa+9C946VgOCiLbvRjRJUrrofFIqGD2Qe3YT8lAsYJqXdU1VZCo1HZ+cJfRuXtKyTN3Qf+/nv7/M8/G0qEJj7mLv4iBAc279pt9kfjycnp4OKs35oTVQlav95jwvCmydsSHM3yzlMcbQBW8QnA8XRbooe+tSK6gIgMtpkapk7ZLWqny5ewWyQK6JrCWXXA7nebvbOhcaM1JBokv00VoDCsJizCWhS6oqiN57YywClFaRjKiq6rwmTChEacnROiRUJGPQxxiazqkSBQOWjBn68O7pcdgacoEbD+NASDXXtCS/HY2h0kpeC5HNqfrgkGi/j5eZeq7FWltK6fuYW4sxDMPm6XAYYtd3IS9Ty9qyswRYazHWISIBqrBI+7Vf+rUQw9//h7/rg5+XxVl/SXx+Ax9r3+AZAXRNa/BeRdBZUGi1GSLhZr2f13Pf9845bu3CdWapBIRoU2rOWgVConmeL5q7rutUxXmPgGIkcUEGBbiYTGtrZOxue3XxIgtLlkpgmRWR1pSidwiw224fn54sURPd39yczud379+VUgFgt9v1cTg9HYyxqTUUKSlpbc8/eKFPh3XNl+z/5eXfagnWG0UA2AzDOG7WaXY+9l3Azq21LqmCKafzO2Yw1gKqi0Gd2XZXT0+H4/ncvOv60Z7TuqzXN9fDcLUu69PxnPK6222RDBpbhj7e3ioLET4+vNvtR0Om5EJArZbb2xvr3bt370RxXXJt3MUeUK2gI3v5nU1TYuG15tpaCH7Yb4gQmgBobRmNWZel7/vL3wgsAVgRvjjYu74ruYAiGiCg2uoltoaAwgpeYrTQNK1ZEEywaU7OhInbn/74czTWOjeO/dBHUielGEMpp66LzjkWSKkAGBFlUbLQ911rzSAaS865Wtvh6dBau76+2u42zkJp8vb+XSmVWb7z7END7XB8/P4vfA//5/+DfyPl3MVYazVIwmKt3XhL1tw/Pflh3O93796+cxecOOClnHWZBNXaWFuM1qBpzMxCQMqiiE3apeYifAGJACI2ZG6Ss1zENxcbOxgkRAK8eM0uVjF0pKotN0PGOSfSck3OxNZEmC+u9suSllELNwIc++isXdeZkNCYCyBKVbwzt7e3X335JZKZltxSsdawgdAFYtVSdjfXb+/f51xjv0EAb12rlUl6H08PT4BA0dda+xCNQ1S8FB9KTURkjQcFY8g5ai110f/KL/7S7//+7zfmru+8G8iYyinXknM1Jjpno/cWlQylWkTxeJo3YQBSNHg6HZDAWG+tt4ZiH+d5yrWW3ETwdJz315suOmuwrmlZCiud55Jb6be9syYE74xZz3MfQojx3dOhNjbGllJ3m4FQCSG6rmlLORljamuGrLEulaWL8XyemNVaW3Ljxn6kseuX8wpASCbn4gGXdbZdLEjHdZ2nKTrz7VevaqoPT4cPPnj5dHh0znsf37x5t99vu0jLmmPoQ4jH4+nPfpoJ1KjiPCcfrHAdhkDMaPybh8Nayu3ttffw7GaX5rwsKwUfY+xraYpogv/OL/ycWDzW9jAt1nnOaQihiz63JNxaKijcBa/KolWpdV1UoCrMykAqqEWkCiOptzba0IcOBEuTh+Ox5sKtOQvGqDUQXIg+RksOAUVbrly1NhVjVIgadtYh8FKW1JpUXJbE/GfCa4OqTbFahG0YPJp1zvOcFFyIY04tGLfMS3BhXcvT44kZcpGk/mmtX7x5v07VNHt7fbdWPkxFwAuY1lrwTrQpMUFTbLYLxgdrfd93vre1ZWMJgJ2FsR/Gfowh+OBLKfO8Bt/Nc/n//qP/dG7EFHOFwg0QEV1w/ThsxqGLlrw13GSZy9PDOSVeUp7z2UYL1riuZzDWRWVomY8PpzdfP6RViHxrfHt7ZRHn88wV+nEfh00RWco6bsdN1+EFfYU0jMNpWh4OR1UG1Ri6Po5rKo9P56fD9PjwULKqOIvWIbSalmUixVbaOIzemlaaCntvnIlp5dbUereUlFr145AV0PldH779bP9bv/ILL6+ulnmtTcdhezgcn13fGkRV/vijl8G7kqCPg3AtdY3RO+dFEZTIuSSNorUx2OBqrRVMZYnOfPLB897Tzz79/Nnty+9+8K38tOC//9//rxCS895aW0uJoZumc22CCChyQYKQNUAgoiCKl9B4KTEES1SaXDagXddN0ySsxnhrjUozZGouueRSmrFuu9tO5zMRGWdUxDl7kdcSKZHhprUyq6hBUSmloqhDrMrkHQCmZbVou9iltHrnVPniX0KiWllEjLWlFud8jHGaTq3mu7u7mrNxzlmbUlpT/hd/+tPduNkMw9VuYw065yqXxlZVn56eiGAYewAQbaVmZbi7e5mXnNbVOTQGVJVFvxEOoU0pex+ss9yqcOu6eMFNLbl45x2ZXIv33jk3TRMotFYtkbfucuazPgjqaZpud1tWFYEQQik559KFrklFxC+/fiugL56/eHx4f7XbATkWrTWvy7zdbBXx9Zt7RdxsNwZhnk6bzTj0Q9/1x8OJWax1h+Pp/v7++vrq7u52HPu3X795+/D04uUra1Q5CyIrlpK987UUQHPpMTPL4+Fhv9mWlFnVxr6UiqxwCUxJGYcuhPD663vrYqvp5maTSyHCJgIAlfV6f/P0/oks/kuV4bKmJSVy3hA6g60UQ6Yb+tqaN9Zasyxr13UpLYh4PJ362CuCNd4hYM6pZDJI788PhqjxpdoE0cfaamOupaac+77jXBDI+ygCKTXXuVISNxFeS2ZEtBZrqUSwrrOoeu+fvXhhjTkcjgjUGg/j8GcrgGoMAWCtLefKKkSkTXJJrcimH0HUE13I+cRggjmcTtIaDuicbVUuB2F0NlqrqvO6+BCaNOdjqbCsMp1TWp8Q0VkzjOO3P/nIIMkl/NdkWhey1HKa5nm/34uy8xYRW0MR3ex3rbVpnjbDiCC1FEVpwoi02+1O57mqoAiBoAECcznpNmZvjQjnpkRmXdKi6wXh23WDipZWyaDroip3IfT9rTZuOddcUZUQubb35/dh8C7E3fX+9es30zTf3t4RwNrgNC8lLVyytXkzjt/+1ifv3j+s89p1wTkfglfVZZ7RmsPpnHP+9re/xcTzPE11MdVYa0qrx2XabTpVASUBAjIuxlQqt7YsiwCK6O3tjSMauniaUyql78fz47Eb7M3t9vHhgbWlLLv9AIC1hJJZBIx1rSSygUW++OreEjh0hgyCrmuqOZWUnHWosMyp8845n3JBMpdkUtfF1ppzodQ69GNStt5brVyZkOTStQAAVyNJREFUr/b7ZV1zziH6WisaZG4+hNJKa02UEXG33YKqsFrjWS7lI3uaJlUGMCJNBL13l8gciPoQRYUM5VKWVuZ52oy7EH0p+c9oDlSrgGqujUVCCNG7kpd/5a/9tZ/89MvPfvLT/WY3r6lyY62Dj6lUMgYRu34oOS9rjiG0mp03F5+YJ/KGDKH6cP9uOp/fWWtr4bvb2+ADc+t8p6qn85MBVMJUihXfat1sxr6PhrCLoZaShcW4WhuK9l0nytzYOV/K8gvf+7nPPvt8mWcRtkiIeOn4tsLeOmbGCwE9J+uc91GUjTUiAkBIUEt13qWSlSgGX0pFVeOdEChCrXUcx8CScgE0pTZj3O3tCwV88/6p5gJIlZtFuL69RsTzMpns7m5vudVPP/vJqw8/CCG+ub+f59Qa92N/e3fNWvdXOx/cMidvwm67uVnTw+m42/YAlHPe72/XnC8q62VexmFslYmwlqZEpNBqRaLD4fFqu89lOp+eAOSrr796/vz5tz/51nw+3b97SkWcM62B833KJS35cDj6YHa7nXPhfD4R4dj3xhIDLGs5TaUfvCl5u+lqmki7VpsxtK4p+EBoBCBY2u9H/J/9d/91Fnn1wavHw9O6roBKxggDgKKqNB4340UfX3PuQvQhPj4dVHVZizFWoUbnSmEy9qIFNwYJUfnyDpbGFUStQQA1xjrrDscTAAQfnQs5JQa5dCmdtaRqVIIzx8Zv3t5fjfvjvGyvdrUVb6iWZoxjYUJc5unu9lljEWlkMOekIiGGkqsz9jgvp7XsdzsEaK16a7yz9/f3oRuD83ldu66b02q9c94jgLMGVCwiqTy7vRm67su3j6UyElhnHp+ehNX6sOvcbjO8efseiRTNpQtrna21EhpUcH82N2nKAkxE4zh674/HYy7l+ur68f7BWLq+vb6+vfnTP/5T74Mly9i+oaMDNG7GuVxaq21dV0WzpJprCSE6MldXW5Had12rlZnf3r8L3dB59/xmf16WN2/fNhHnQzdsx663wrVVUa61BB/fPzwF5692ve3HOeUYnHfms598zorPX9w9PT1aY5lVLg5fAGNIVbx1udaqCgAOzSWhFvvuzds3xpixG1Hkzf397ubaW5trqbV2sZvPs3Hexy6lfDocAeTqahe8Mwan4/T1u/P7uWYB0PydD29//Vd/7v7zrwGRjFHAdU3eOSLT6rrdjhaNMca8eXt/wdAZpGB9A1GAi3gzpeJ9SGsu6zp0PRHWlonIUBuCB4yARrUCqLPADMyt1OasVxFrDJFFA6CaS17XggjDsMmpMMu6np3zenGIlXypMjpnpyWvTTa7Hatuxl5K6bsu13I4Tbe3N6iccgo+jONwPBzSksj6WpuxdDpPxvllLcbFl5sNkSs5zymLswqy2W0AXa01dH6z7ZWUWQ9PT3c3t8hQawVDKu3+/bux70uppTYWsc4B4GY3IhJr++r+3tpYWXPOse+4VS7VIRGBKDOq8VA4O+eQyRpHgA/vHqy1FmmdFouOa3n57PY0nblVtX1tSBbK2kSw1GS8rWuqFQwgM6pRH22IpvPOGwvSWmlvj++cd96HzWY3L2sp+bye+m7wsR+s7WPXuLx7+7rvtk+Hh2fPro33Rer+esfSKomRFgDbmmpWF70RgyRDF8/HycZ+SevxeHh+cwtSvfdNZdzuGvMyTz7Yi157ms6ivOk3pVULLnQjgBUwObd5XvZXN93QT+fl8HC0Dm9ud97bdU3naVFmQL263duR3z4+rXP52c9++vPf/vBuv3tz/w5jYIVLad5ZOyu+m1b89//2v3Fh019iz86Qdz7VKk0Q4F9aF9aUrEFrDXzD+7bGGK61Fia0IQRmXtcVEeRCpkW01l5SM6rALDln54yqWuun8ySitVTn/e56n9LauI5DZ60pafXBGdufTtMyT9vN9ng4krWAaGN4fHrsQnTetVJyztvtRgoH6xQYEVgaGteqeGdDdMuSa2VubbPZEBFrm9YUnBNu3lqyNte2rKszNjhfa3XOllqMIQA11tRSg/fGmC52zA0RC7eUyqWLYhBvbm6m6ZxTNmhZ2Ht3mQ7/GYFaEHFd167rDBEL51yQCBRzboYIDDJXYxDJlNxEaZpnFwyzSoOh69ZldtFbb5wz3Bqpvjss1kVujQh3u61wK6UFT/vtoIqH41kVj8dj5TIOo7BsNgMgxOiYxTnH3AzS6bj0/aDQkKSUuqxFqb66e/Hw/vD23dN4dXU+T70Pr57tS021CQDtrvatlJLT+TyHEMlSrtU613X9m6/fhNi5ENZ1JcRSas35w1cvnk6n949PXRc+/uijr7/6+sXd3TzP0zyDQ26tlfZX/up/9nd/7/caNwRAxbHrCPFqf3U4HlKtNobruH16eLAXg1Fp7YINr62VXDKzd94i4Z815lzw49Bzq0AUu06YSynGGmv9upTT+YyIePntBw8AAnoB2dXSiMw0LZvNqAoi+v79w267c86dTqcQPKr0MQD4Na1syTnHLISMoNwaEl1d35zPpy52AvrJy1fCzTn38PA4bLeIyBbRmFJaCA4VhNU7z1znORuy3rsGUEq5+MSttWgMSss5R2uJqO/7dV6dB7SmtGZ9zDl1fZfSHIK7tP6XVaSxIcosaIy1YA3dXF+nNQ0hGiREc7l2OedKKaDK3CqzASQwKEiWKrfKLYZuOq/edqU05OY8sVYUOs/rvLbT+Xhze3V1dZXmVYV3V9t5nh25byIvJT+72rUGpWoIzmjjllpex7hLKZ9O0/k8A1I/9B5C3/fW4LIszvmca62VmUupYzcyw/E8DaOP1pVSlCV2V4/vF2Hz/PndUpbnz7dD8NZYwI4lv3nzxjm32461JK4VQ1dKJYJaytD3tze7949PPgZrEJGsAePdPC/O2o8+/uD1V1+/f/9+t9spwND3pZan87LfjETt9aefDSbMbJ6m5eZ2P5fUOVdLLiUZ65zz55azRRtjXNaFDAEAkb3En6LbWOuk1tYaAo7bTRMB5mAtGgPMrZTOe25ca2tSFcT5oCJNNOc155JS8d5Za210AECbuN12oNpa67trZmbJL17epHUlrWlJ4zCCD621mhqg5uUQ+w62fWnJWT8MQVo1CChoUOfzGr0F0MsavQE0EWzCTVQV0LBoiHFdMqKiITCk0GqrwjY4r0Cx8zmlxsIKtTGsqYuxSSXgPliU6sii2mWdQxeXNXvncmNDSKDG4TgMteRSak7FGCtaW2sX7M/FFZFyUjTcym4YpbUK0qQBorA4i7EnVVEWYNOH4eFwfng4Kvmb27thcMt83HRD8I4Q7HZwLjw+PhqkLvY1NyTNraqjOaWHp8Nutz8t6zrP47ixzm42GyIVaVznVpAuKXLG83kax77r4lrSvC6s6gIxV2e9s+oIKrTdZox9t6SgyljqIZXSRETG3XVjWNbcx4Gv9P3jk7XWGOudPR8OV9vxo5cv5zWLynmarq9vCrfztAZve4zOOG3ADH/8pz/a7bZ3d8+scaBINvzJT36mYOdcfIjTw9l50uBPaR62m5qKzOvrh3cff/wxlVpZ9Gp/5Z2zxlsKQ7cziK1mJAjexS5Yay2RtV4US+V5Wkup87Scno5pWSxicMZbcs6oMqB6Z17e3T67uR6HLgY/Dv2zm10XnUIzDochIqp3Zhw6RBBWH0ITdtYYY8k4Z7uuGwgohkioKhUBELByW3LKtZIhMlRaJefaRXxuEA26GIy1gABEqQpaYywZg0hgvENrnTeprKVVUb2kpIP3wfuxH5yl6C0qXyas3rtc8jgOKheNC4TgjTGIYKyf5nla5lprKaW1PI6dNeS9s9apQs71eJwOx9OrDz6eUz6va2l1WVcyiCr73UaYAbSyKJkvX79V1Bcvn8fg7t+88dZ+9OpFHwwanPNKhqyBPvp1ySXBWvjTL756PM5LksxwdXuXRUR1GAdAffnyLnpyFskCEHYhRu/TPDljgveltDVVUN3ueuNwWVMpnBOPwzh2ZjM44fL1168fn6Z11WnWp9Pku+BjEOHK9TzNh2kKPrx6frfph7xmEe1iBEWD2IdQGuQKLOKscZbSmnNpu+22SpvXdbPdrWt6fDoQQq5ZUD/+9idd79d8zrLY2L17OjofABAEdpsxOrPfb+8f7u2aq/f+fD4rN1QDQKxqDCAhIV6Mnes0e+cUrSjkWkor0XnjTD/2wnrx5XBjILzENH10zqCqWGNEBFAJkGsBRCKTcgUkMu7t/aN1tmrz3gEIgyhdIlhMSKByGZuDKguQIWf9mjIDGwRDRoFSrtZaY1DQEEKrVUQN0bqsChCCD8G1WpmbdcGagFRFwAVfmYnIkC2lOmuW+ey9sxZTadb5UlvAthm71jRa37ghKLSCzqtAqextQNAlL5vNUGtZlkkRlnkVmY21iBbIiPKPP/t07AcKMZUc+267Hd59/cb7eD6lz7/8ehw33UYrAkrdbvorGs/np6vNzhtSZ0VpiIM1aAyhtT/+7Ec3d6+8s1c3N10XUlo3fbSOACAaa8k24ZQWc/H+kEULhFS53tzscy7Mzbjuq9fvXt5euUDX+2FJhZlazSUvfbh7//5B0KTarHPL8en2+mo0EYl778raOj801TmteV42/TAMY8qp1drf3rTWluk8z2laZXN1gwZ6b9wwzKae1wUB5IKk1Hb37Fkt7bOv3sbOx1puEF7dXnlv7k/HL54eLeE8rYOjyulhmfc3Ny82m4fHRwsAzI1ArCUAARFBMWQN2rRmAEWiEAIAgDRniIwbbcy1MrNzDg2wKIDJtba1OueC94aolOaCE2GylrlVQIPWWVtrtdaqKhHE6FWh792FPXQJozCAKldWADydGxlCwMutq7WKeGnxkQ2emGtrFiyvxYdASFUqADZma40Cisj7x6cuhs1mw6LCZdcPRKaUWnJGYy7//daHVEppgsYbC8uajHOtSeIaYgcEyBfDhHpSQ3TJ4KgqGbOWoqCtasmFlWOMoqLAiu16t1/X1Lg6ciyyzIu3LsbN6zfvS5Xvfe/nXr95U0vdb3cPD++D62LsPvzog3eP77wzffRe0QkiIiuel7XbjXETIxlHiASMej4+7nb7zoaS22k52Ri8Nc4YImylKgh0zjk3zdPQj1HAOr/fDMPYGwcpZ1DpOhtDvyynL+7f9pvNNE/Wm91mKMnM87GLIToLCsMwiEhwfuXllLLpBlfKZrcn0IeH9znnLsSu60xPLNIqV6Ipl8rNG3p4OorCzd3t2I9djA/rAzna7HbWmLW2eUnOOQP44fMXb998YTxe3V2lNbWGf/qTL3dj98GrF5YQDaB1BhERyXiqualgbe1CxUWkC7fWGrpEi8paqogzFpFqKYpESD5Ea6yKiigCKGDOVVEBUBVbFSFq0kSUuQJcOD9KhLVWRDIGRcWQAVBEuJi5iWyttdYGAAiw1mys9cYpK7MaawUUVJ21AJpyds6dp8UY8t475xvXNaVUqk5TF0PfxbSu1lhFg4iXzCwSrWnpYjSWgvcpJR8isxCSgrbWuDYRGYd+Xqbj8RRjR1iZ2VijiFwZDBFS18VL/dqAKSW/fPmcmyDi4+Pjy1evjqejKKNOzvh+2Bzfvj8vi3Gu5iTBDV3vrFMVQiBCZ22tPNWaSrGEfd91Pn5y9wIVWZVZHJKxtA3bVhuR2e+ua32c5oUMXm+23jlWCaHn1mprSPbx6XB9c5PT2gVbcjGirWYC5FYaQRdCrui8iy20UtZlcs6TWFVCtT74p8dTjPF4fEAkF6LvOpQ6nY4q/Ozuxbyu85RSWuc2O+eD8cens4/eWOOdv766EiVlfP1w75359iefDJv+7ft3fT9YQG+tcebF89tS4HozDLFH8o+Hh8fj2dg41/bucLT2YmonrLWqMKJRReGGiCDYqjROLOq9L6KAUBrX1oLzDn1bmja23ggLIhCQorJCTgVEWVmAffDe+rwW0GqDy/mbxj5i6Pu4prXWBkAigEhoCVGZK0O1VhEp1waKzG0YBnSmtqqISARIPgRBEZaqoNyA0NiLrAMMofe2zMU5b50lhNaacEUiUBFR4xy3SsYAwgDmgppqJUlrKmKNsxaMMdzYOQNKoi14w+CralnnkvMHL16JCCA1BDEgTboYW2ugMHajVDaW0Gjs48PT02a7tUTCTZXWZW3CT4fD9c2+86MluNpcHc8n4wypoJI1lnNe5xS6LqVVNYHKbrNdzifrQmFZUwqdN0SGaF1KpoqIYAw504BLSzGGUgqIikBtstnthQVAuy6WzNaaGPraRFlbbbZzXXBc0hg9OzMMw/27J0U6HtLj4+yCDyEK2dAPKNLSWtI8OLvpIqJdl/J4nH721YONkXyNCGPctgRVZByjE7Omkisb6+bz4m62a1491Fd3N6yolR2ZXNNmN2hrH9x++/7N0/18eHxYDtP8vV94MRr/9ZdfW+ecCOdUnHNNmJlDiAhQWxPClBMgRO9rbRf3ZqnVGNOYG68ISESWTGGuwgqAoCBgEJqyKhtDXHlpqbamotoSAgIAt9Yqq2CtzdqoqirNGqqleO+sscBWGB8fj4DYdR2iPa+rJSAyF5gpMmpr2tqlRB9CbI2nZY19n1KuLK5xLeX6egcABrHkhZCInChbQwJKhKVUIjO1alC9sSBMBNE5ImuJQEG0PRyetputQaNoQ/C1trjdKzTjUXIj60kZpAGIMDOzc76xlFJMBSILAvM03VzdqLbPPv/8xcuPlpyHPnhvN10I3s3zVPnY9Q4QycYLKYh8uL3pSikabG1MZB5Pp7TOMWKtLUSfk9SaK3PfDWoFkF/d3RJhWRdVyevSj33KxSJut9vj6VRB+xg92ZSL9eHwdNhutuhMrfU8rV0XQLHUmlJ5/3gGYyvrxZ7F2IoqgjYpKGLJHR7Osuu8pVqmz79623z86OMPtZS1JUU9raftbnAK09P5uKS722en5bhOpzD425srSzRnJSPOmdh3IlwbBhOWpdyf3zws5eFpvrnd/NKHn/BaH9fjdttZRBARVSil9n2vKpdhu7FUSzaOrHUqYq1lat+82kR+8ed/+evXr0/nSVRrq5UZDDGzMhvAxgz0DXOicSuNY+gYWkSs3BQNWs8iU0os6pVi5xsXURGU98cnZ31NYqxFIhdsCM5Zi2CW+Ry8JyIWITK1Ya3CLMLwOB+6Ll4yvCF4Zn58eorRT6cTEXUhxBBTSghISN652iorGqTamkVjEJ/dPjMI79+/F8FaagU0xgCRMe54nvp+8METgXUkrRkLQM13Ji9VBM/zaoxxDktprQkSiiqBWabV2mBN/eynn4cubK+u52U1SD6Y3W7TcllKLrm+eHUXon/37n1tNVcFyTFERK7CLCqszKwszvSg3HVGRYiwgr64u0ZSZx1K5w2gIjoXYpjn8zKtrDqEruUMBIAUfGy5+Ohaa7vtDhFTWgGx70drL25CKaUhUi7VhhCd98HlUlX0fJo6762xp9pePz42t/vg2Q0RPb+9fX9MnNf1fKpCu6uNj8QtedcZ43M+TdN8d3P9eDp2YxTmmjKSC945e4Eem3nJOT8SYQO+utmOm6suCGpqCVnZ+mBrqQrAzIg4z4uCKoCzxK0ZQ85YRBVU1QYGCDGXGl14++ZtmhNWZWlNBQillotGVpFEldCpgoggWP9N6d4UKSlnY513TgmEoQuhljKdM0sd+uHVBy9/8pPPnHW2UxecsX2tWUphlkt+UVttAEB2WhdRbZVZBQGttcMwpHWdz8eu64TbZR6Z83yRj6WURLSUBCopXRDrUQEARQFSWl+/vefWRLVWMdYCcF2qMURId7e359O5tNqAjXXc1LHhlhtXYRKx56V5BxGMMBIgNLHOPJ1mayyxClE/jl3fISiXBs5sxs4Yo1aJkIw7HU6ppC52xhhiza2CV+usCOcsm+2Q1rK2tQ+hciq5hdApYPShj32taTqcfIjzNO/3OxYstSqQKu6GsZaiYBBdE3k4HMeuJyLvXF7zuq4+hBACC5dVuLVhGEGIFR6Oh2U6DdfXNWVvLUsL5FCAjIkdfOe7H6I2BEipqGBa02mdt9uht31vo9Yc4jDXKggfvXxOQGmevTUP797/3Le/3VICKc54UAGgNWcGMAZt8PthmI95OUy/9Os/9/X9zx7adL3fOwQ7nWdrrA++tIakaKi1hqAIYMgiQCklhFhrbbWIgrM25/z1+WtrHSgqsLGGaxPRVy9fHg6HJa2x63Kt39y+FcmQd6HUMksLQ4eCtTZSJQaLUrlFZ2+f33311VdfzD/tbHDGM+m6zE1FhYMPBLDWbIMFhJSzSM2lWeecdw7AGqMq67Jwa9/gDIicJ0R89uyZtfb49GQNdjHOKXfdmHIN1p7O849//JPdbvvsxfVpmRy3EKOw9puhlCQs1pmu6wjw7u6ZMB+fDkVaKkv0Y0EFbMH7VpiRXfQ1F13FGmOMGTbbN2/f+BgN2dbafrOxzrEUBO36TpVFhWtDonfv3j9//uJ8PAGgCoKi/pkRsK6rc7YfAnNxHn0Y85qd88uaHYQf/PAn8zJ/+MGrzRBjcGChspzmKfggzCF4ZhGAJpJyUoOsuu17Y2wthYkQMcZojLn0t5yx1rplTUCY11WEb26v+yEcHh+7OG6GsZSSc3HByNKwifWOS+36/pyOEO3Q9dHbzoWnw6Nz0NT1425eE6swt9jFh9PBWRusE0gMfDqdvI+oqKB91xEhAeQ1LWsyxnz1+ksbadz0uRbfd/jv/Vt/cxgGAaitIpEisAipGiJDBhqzChBdqGBwsW2K+BhCF8+nswIgqzA744ioCZOhJgyEIkxoDGAplZAAIOf1+z/3869fv5nXZdiMy5qXZYnBEcGFdotIrQko1pSfv3hxms+11hA7YSmpVi5I6Ky9sFgJUUWJYBz70/lca/PBh9CzsAJIYyKstcQY8pqcMyUnF+yaRdU+Hs9v3z2Grru63u88GGOcjzVnYR66UNfZb3bLmhEhpXRJwTnrhKpxNi/6+Pg4DCHGIE1LZbImrVlEL+A0QpPSur/afpNWda6UXOtqrDVgAMX7wNpOx7Nzoet6LnVZU4ihtlpK6WJXUwJC6z0rcGu1XngqQKaGIR6n9unPXovC7e2NtBqcefbs5u2brw3Jt7/1sUWopbBoLsWRJSKwVGuN1nfOzzWBiCF76e2wiJIxYFprhFhaUdDMTQkNYvAeGS7PTmY5T/OwGRiYDPTOa9V5TlOtTBJiWOec0nq9H50hKFCEC4lHi0BrKVdXeyyNhBNo3w0oyqWKMlk0BmsSoZoKKAQbcgxaVhvjMM1n20ALiFRGBGFRRYPWGqxcayuXWYx3lkAr8wVd5q1VBlIDQMIMAqrIIqVk670SaGtesKlhgFSr85a1ca1dHD7//Itcam0tt6NxxgZzucLnUhEpl0zGCMu42dy/e2edJevmdQaFeVn6vgdQciav2VuPiK024/zhPAOS9U4UclovgQ1WXtemZDS3x8OJiIS577vj8Xhz88w7+u63P2gsrTZjoyFT1nSRnCxLagycMpISwjhGFh9jnKf04nb4he9/7+/9g98DbP14+/bt27EfubFDAqFcymla1ECW5gm3+w6NUYS1FkIKIXSxO54n733KBUC980jIXBk59t4HX8/Nh+7+4anrB0+QUj7NpQoCqLT1ej/OSzVhYyT/ync/BKDH03kF/frtw5LKxx/sjZSODKI7rvNu33NZGqtzsVY2ZJlbJm3ftCGc8b7mbJ1REUOu5OK82w6dcW5e1iYAgsCcSgZ158dl6Pzv/PW/8ub1119++bW1sYqeTickSlw94G0/2N3tz372hQUvTYwBrSVSJ2qWdemDddJssJnheozBW648n0tZuY9d1YzW9aEnyszaxU1O8xCdSPIklgVVyXoCVWEWAVGe5tx1HRBWbs67Sw/OIDkfuEmpnFKa1oWsYdZW69D3IELWAsKyrNbYquiDg1IdGRVksEttc6qvX7/uuk6Y+z5677yjqhkRnXeIGGOvKhTMw9Ojd947m9ZVpBHR7c0uuFByNkT9dp9zElAHFxCFDMPQGrfG87R0MQoLkjPGNea394+gOo7RBPSBXn3wQU7ZWxO8b9NskYK1tTYBqdrYOXT2MK30uHYuooGrq30t6fg0G2vffH34+svf9ePm7tnVcVqmua3ruQu+lHZz9/zxZ5/P6fQLP/99QoEmnlytLZfcdZ2qppQJzX47lloRDBFlRBWd13Uz9IbMMq+ImFIurW2CDxaflmOuNVXJKe33/c2z2/zlW2iyGzcpL4BiDcYY7u6uAVoM9uXt3bs3772LXee0SR83D4fjaUrb7c55K1xTSiF2fYyPTydhsdalWsZhWNb5o48/PB9P5+nM6wJoz+dJxIfgl1RiZ3f7nutyfdtHfPH5n34WQmxVWi5XN89yO7bWnp4mMvN+1wOAihXFxq1om9aJUHbjZrvpfIjnaSFWZK0pOxuoD8fp6IIJTsmAChuilktKxTnHVYLr8N/9t//LXQy15mHshfl8PCISornY4kAVCYhQWS6QZQWc5yV00TlXW7bWXnRHtRRVTSkxqLVOES1ANIYFHo6nw1zmlL2hEMNmM0TvnaVSUinl4lo4HA7DMKSUjLWlVgTY7XaqgqqtFm41hjButm/evrXG+hDWNXnnW2MiCMELQL1YxNGsKQXvULVUTiVba672V5e+DiuXUplVRVuTaZp3u721sK7rxaanKn/9d/6aC/b3fu+fvrt/v9luLiZIVeXWEI13AQ0eztO8pMPxsNvvr7dXaV3UQirLOA7PdjvTCqo9JP3BD/7k7vnd/uqqcWVuhLANznc9Ik7Lap0/z+uyrsFgH4L3MZeScrpsdcu69MPmzbsnAdta4Zpvb/ZWFRWMpdCFTz//XNGM272ojH2sad0Ow/Hp6frqmoiYxTvXWOd1UdAY3W67medzF7sPP/r4j/7oB6LYanXWgkKR1PU9AHzxxZcK9Od+/Tc+/ezTeU193+c1jcPQx1jLmtISyCESEwAaUMi5PpwO3vdDiCGgtaoKBOH+/TmXmiGnXF/cXb+63d1sh1IqMy7nJfZhXZOx8f3jk4/OB2ONApAqehtyzjmX2oozQVjxb//X/6azBCDe+8atFXbWlcalVmX21lxGnWSJhIBhrbW2aoxx3nlHSERk0rpeIhmp1tP5vL/eGbws0N1nn36ecu6H/vnds83YiTRuVZWXZRWBrutB0VoLCmtKSCgq3ntv7TRNm81mWZZaq3fOWT/shtP51BqXXIdhU0ohJGPtMi9LWkOMrZa0rvvdVfBGtCgYVey6bl0WEgECJXthkRPgJSC32+0Ky3SeQZVEeu+2ffjzv/orr5f5Bz/+SVoTAIpwjL7W5q03xjThlLILYV4SGduaNG5X++06n8ehzym1XJfazlUcURfcZjOWnFjFBxcVvfdIRlUbS2t8STyUXPquJwBESTk754V5LXnJvGYWlfPx6Vsff2RBRJrx/ul4ZFFrzKbbsBQy+PQ0bzfbEEwMtlVAQJFqrMs5jZthmuYQvIiUlNecnY85FwDoYlQQa7G2JoBPTycRePH8+bqs5Ksh502XlsVaqrW1Rl3nmmTfj7nW5TS32kwMzlpqDCoi7Hx8/e7hOKerq/1u281rena7C8B3V3sE83iYjbHvH+93V1fzWlrlceyN5eB9WvP5NDsX2oX8xa2wGOusc2QM1SqtsQoAGlFKeYkhNq1ExMJS1SCt8+qsFZVlTbGLWhTVWOfWNVkyqvj+6WlZlxcvX+ZlDg7B9X/yo8+qyHc++Xg3OIsKpRhSYzBn8ca7LvbdUPKyrskHT6jKAqrOuOn02Pe9JbCGjOmEVRClSEA7nc5dP67n+XQ+A6ALURCM9aKKiHfPnhFoCH5eWmU2xp2Ps7POWvP2/t2wHUIMtbQ5rbc3143bcj4+HpJxrutiHML5fFw5/d1//I9yUR86UMNNYxzzmkXEfpOOVUsOxfRx/OKLr8mbZ8+vW86BfJ7yWjls9jrPNxtj0VgkT+S9r1wJqKkawOPDw93z511nL3T6ldl4u6wLCl/t9jYSgDQyZHukQliGYbMJ3hsTvF/z4oIX4JvbZw7p3Zv773775X43/JBfD5seDOc8M6M0Dc70Xffs9u79+0fnTM6ZWWLsGcx2t1tSmpe5qRiDKjx0fWW9ufbLuj4+PvahW89lO/ZprYhgrYTgUhZLSODrmksqWhgFWqrgWiAEIRCH6Bprt+3Gfb/1HqRFS9iaik7TnEujoH7o1pqrsPXudD7eXI0W/enwBGhUMaccgs8lm84pNltSuzDiQogxRkSttcZgCaA0XnNB4hCcFrHBWWel5KFzz5/dlLwqMxGpdaU0beKtHa6vghEKQcD+9NOffvTi7ub2qpUMwqnULu4qt7XkXFhau+mHVpNI3e0HBQYwSLZUYVbrB1Y6z6s11hLlmrTKU25Lln/xxz/76OMP9zfj29Px5cuX1/urcp4Y1EWvIio8L7Py0BiFzLxM+65vOT/m3IKN3zzzLRl/nNbYxz/44Y9+/ud+kVuLMXCptbTN7fU0z89vr9a8eLLKpq7Nok3azvNMaKzzxvpzWlX4+m47dF3Nda3FOouWxmEE5uvROmdKdsfT4pxuBmvIGfRZL9YBhwCiCojOWnIWld4+vem7vqkCCKEA+rKuFuHuepeWtNv07x8O1uKLF7eff/ElIh0ej10/JDCffnH/ctl5a2peAEnF55SHvhfO0qSsqyPQKtZa70mxoebt4HOeQTiGDpsc17yss4o4H0qqYOwiYL1DB2PvT8eEOJTUHNppWcnaeV7WtaQmVRSMIyM3277z3iPkfP65b3/wcDyWaX6n8zjElvJuO865nddUqxA6Eeq6bjo9jFeDJVqTdpTubq9ePxxA6riNSGYq8+N9HYc9GYNEsNmOfxYLaSFalsbA3rub66sXz5+NYxe7GIIjBItwe71HqNfX29qSMWCJDEJpRRCqqKBloJ9+/vlut3v5wcuU0nmapmXOtdpgBJg5bzb9br9x3gIBkJ2XXAormSVnUak1s9TGBZGWdZ1TXmpbcj239WF5ePnx87u7/c12+KVvffzhfqcpkTNIQEhk7Os39/PaRI3UGq0DbsPYMza0stkEAqk1Be/66KN3KPyf/1t/KwTXdX6eT8bizbNrUXHOHk7TtNZ3T+ei/LQck6SZy+N5KqxrqbW1h/u3Q+efX18ty7Tm5JxjZkLgUg3g0I3Bxxic91hrdi6Aakrz4emEZI115/N5WWZARQIUrKW+eP5iHMdpPpOzS2ktL3/1t35TarGoMbpaS9/3/TC8fzzkUjebMXjTynJ3Nez326VKBVhSUaTamg9uLSkxffH23ev3hyyg1jjvRNgqd87+4I/+KK1La63Uepqmp2U5rOn+8bDm7ILd74bO49h1lmiaJussGWLQ4zIf1/m4nl30TOKCUW2trmMXRURBjCVnbWsthOCMJXKA1rpA5IgodmEYu+hsH7xwG4bhfJ4ac2WZanlap7UWBXBknu32jglMe1zuKZWsCNbSvJxbywBNuXR9X2qb57N3sNsOJSWRbzhnCGCNEdGcCxrTBIDs169fn85nIFSE4zRPy3L34u75yxdfffFFXpJ3LsZ+s9uteQGS3W47dB0CLOtamUVRgM5zzoVrEWNt5arKzlmyFsj+9GdfNFYmA63d9N2Lq53B9vDwpkFdcmYEcmZJa6n1/u1baz2QP88rt1aX9PLu5bSsqZXNENu6VBZD1FrJec15NUTKcjqf1mVx3paSADTndD5NLbf5sHrqHLn9dnSWhjha65GolHL/5s3V/mo7jq3koetd8MaY6PxFTFhyyWu2ZBGbM7LMZyLjvBNlH/q0FiRTmZ+eDjnnlPO0psPxuCxr8E5VhaWJBO9+8Cd/PPQxeEuGhqH3wdjgp3V9+eqV9347Dtf7HUJDxFQZyNgQkMgHj6jRR4duv70GNWmtyDD6OMR4WpuaLqt9OKdK/vM3798dl6VUcn5zdf3wdPA+tFaFkyXKa6659F1U1MoNrLu6vu76YcmJVbo+3lzvXt5dkfB23HjnGjcXXK3FEm42o3XOGjfN+f7hsKw557qm1FoT4VLLsq7GmFwqqH7n1UdXcfNsf73M61LKj774WQvu2fV4Mzoahu12s1vW3PcbZ50z5vmzZ8dpef/0FEOX1vJw/94aJwzLsgoiGvv+3eF8zG/enqYEX3z98KMff04+Pn/50nlLBsdx2GzGcRi45E0/dD70offWt9out+yWa02V1FgTGkNpKZfUD2Mu/HSc3t4/shKgKUXmeX13/3C1u95tNp0329hbNd7aw9Ph9vaZdV6J5rV89cWbGDtjKOU8bLdV4fPXX/f7Yb/fnR6Py5rROIf+ex98+3ie0ThF8DHE2Lemf/AHf+h915ivrq5yzoRkTVCm4zzFzebLr+5P59larKWcHuaWWvDm5fO7zWbTxa6UNk0LKmpjS2SAog8hxCqt1lbWcn29CRHv7m5zrs6afvA+un4c1nUlMsM41sqn03xc1tfvHohIRDbDYIztQp+rHE5zU11rdc6JNiJV5pfPXkht87yAYC2tJOZagyUuxRDO01yrMHhRIMlEwsIKuKzp4enpOE2Z4ek0OR8Op7OqjEP/7Nn1B89vOm+MalpyKSxozmvOhYfNGLqwlpVVUk7eOmJMx6UPfR97FRn6LjjfdwGEAVRUiNBZCtYZUJH6eHgyzrHgmmot4kyccy2CtfI4DCG4bd9Fb8u8SOW0LDHEWptxDgxpAy8O/1v/pb8aQ9hutoYIROZ5BoDXD08vX77oL6AIlLW0VNl532qppXljUyqneRUia613LgaKwas2gwQKwzC21pyhvCaDF3jgZXlqWmvOumVevY+pFmbuOktkWfDTTz9z1n/88UettTVNCERojsfT3bNnaV2YcxFtCkPsuVUVMcbWLIfzPG6G0+Hp+ma/zKePPv7o3eNpnpfbq4GUpml58fFH/+Jf/NGL/dWLu9t302EzbJi5lGyMOZ8mVdrtt87ZWvO6pJRzP26fHo6H5Sn2OzQOOH3vW68+/+yLpv1Hr+5amacppZS3u22pqbUClwCKtQg0DH3jVpUNmPV0/MVf/f7xfJqXusyJtSIpUVRVBODGqkDGLMtSUW/2V4HQIpwPx24YQtdPqRAgARuCUktKmZwNoVMBVWmNAWBZUuW2HftWknOhNF7XtN3tS63ekjOSciXrl5SRYDsM0bt5TVL5oh8DJAJIy7K72U/TejjM/bib1+W8nF+9epHn83YYrDPMFYCWJYmSQWpNamtzWnZXu1pqzuvL5y9A9Xw+brYjAi7LbI1HAGOJWUtrqtjF7vB43Gw2VRsgKrehj11wXFtKGUCnNecmwbtgTS0FjVVhIsT/0b/9N0CRC0ffieCSiwg8u9vVWo6Ho/deGJyPay6GzPF4VOVacui6fhyVKDrnUGstwVlrTE7ZGS+AlTUtU/D+1auXD08PAtpasdZeFHWseDyda22bzabVRsaczici2m43nizXRsGyXKjA0vX9w+MjGlu5eueC9euSliWH2J3nlQJUEUn64asXfSc5TbVSWZhEhuvhvM4uDLVwdLQsJ+9NK7DbXIm21jIag2RH5xjw3cPT4+lsrIvRq+ickyDsttu6pm3ogPBpPg0hOjClNVEQECDw//+a3qzX9mW9z6q3+vp3o5ntmmut3Z59tjknx8RO5CRSUCwHCDikAylcEMkiQkLcIPEFyHdB4tIICXEF4gIhgQVxgrF9kt2vZvZzzDHGv6m+3srFNF+hLut9fs8jWS2EIiitSK3r9WqeJ6h/YelmjDtrh/V6d3gmXBACRoj9/rBaDbVWYDyEKIRwdjFaj4fj0Pc5J2U0IjIhasVofd8N1rnDOA/rrY+j0Zwz4X0suZIXsQQpijGs5CUDk1NUUlDBCAX0mTIRKsaSBaJh3GISjJeYY0xt1x+nqelaF3zKaH1arJdK9J1Jyb+9OFumuTFtTDmnpLSOWKxztVIp1f5w6LoOMVdKpBA1pfV6FUOoiDZExiWD2ilI+cWCD7FgSkVrrQjYEJHg6bqjFSnXh8lJxUMIh/HYmEYIQTKSWjljpSAHVEoIpIXUKhUPJbRGY3R28c+jLSR5FwCRIRJBKWd9Yy7Oz2PwUsqm7TAnJTiwrlacpgkk34/jarVRUuSShFAZoVZ+nKZKaq9EBoyJPO12lNEKEAIClFKyMWoYhnleQgnRBxEUASK48D7t9/f9qq+VpJiGzeqw3wOQ9bonQIQ0XDAKBCpIXpajJYQeJ4eprnpjTLO4ZRmPbdvFuEjJoHDNJSn0hfavWADIk4sxZS60Utn7wDSf53FYDZWx4/4wNG3BSgpiwkCTaKRbpq7rpRAxekaAKg6ULvMshcCKjdYUgFB6ffeY0nK6PfEuGtVUQgglwVlGSQjemKbkpCRflmWcHBOmUsGEzBWd86YxvPqYsumHp9E65180gJ1ZU0bGw9hqnVimjGXEvuvtvJRcOOcvNznKWMZCgXtMjZbRei75qh/2uz2jIuWimzaD3y9LIfTpMEklbPA+RKPF+elJyX5lOiw5pTBjMcZgJakExrkRilM+zbMGrgiNBTllyXtlGmedEiKmwipQJIzLw+iBUsqo944J2hppjExLCN6fX15QRhhU623bNzW6ZmhqiZXURkmfbCmYgJZaOQCbZrcsc9u0Ph6l1i7EcSqHca5SSy0pxVdna0WrL7UdVjVFRurQtt6HFMaSye5xef3malnscZ45l92wrkCCnQQlgtanu9sQYyPF4v27+/vFLpQy07ZD3/vguRY1ozF6se75+bAsy2a9PUy7izPzcP9wdnExTkvTNnbxpZTtdhN9LLkAEC5K3/XH454RlmLcnmzstFDKsTLKkvdLLjrnopSuJWyG1WKhlAhc3V8/Lc5/8vbKOrvenux2zykjUM6ROBcE54SQoR8IUOcDADVGuXE+PztTRjHJ9vv9er1CrI0xjVF2HkspwQelFAX2Eo58enxgXB7nSTIBlEopQoy1Vg6MN+2wWqUYY4pcyFprLeXtqzMKgAGhRobp3/r5l0PX//P/518Sphzmp+fnYdX+9m/94ubDNzmzmEEpVXKmQCgQCui95ZwJwWPMQjAhdMGEoUDFRhkOsDaGAAQbKZOcqzksdhyt84zS1WZjF8c5NEa3bbMehhw8AWQEBOfDMByOh14OFSDnHL2vVIx28sFrrZfs1usVkhLGGQhJKTPKkdBlccByygsXEkj2blFatG3HKNSStJYbvhnHaTN0QgKQ6uzSCqYl14qHkCgQqVVwHoEoo+E//ff/Stf1XTcss40FfUyztX6JrZFn5+uhk6uuSSFyxmMq1vnT7WZZFh9iqZAK2sUpIbEWKQWBKoRCJPNs94eRAsGcz87PCNSUE6HVl8IZU1wOff94fz/0vXcuE5BCzNMyT0sp2K3XlPPTvjkc99o0h/HYtO1htzdKM8aUErFELmTbNY3WJfrvfrhFKjZraRfbN5sP13f90EglaIHtSb/b7bCQrltP00G3KgA+3e23qy1jKBUVghOEefGUieub+/V20zS6loKYoaAwysWopZQAigvrHWNUGZNzxkI4p/Myd61hTCyL55wZbZbFLc75EM/OtpyXnIoS2rkAlPmQjNEMcgihlMoFF1KM08KFaI2KMXHBx8Oxb9tNv8o+fH9zJ7SmnApOleRd00TvuNTL4pSUOSXKWCWkkJqCH/oupVIKKqVzjpQS730KWbTmcBwH3TS6+/7j9RTC6bCa7CwE32zWBLPibJmXTEpjmmG9msYxp9gqo7V+ftq1XZexFkIOh8N2vQ4ujN5SIYUQwQclZde1lFbv/KpfW7vc3z9q00QkD4/7ENwnby6uXr2axzF4t+771dBfX39s1m1G7n1ct6Zv5DxNhPHW6JSS9QEYTwk54946wihQ4MNqk3N5eHgslVBgpJJV119ditXQKk5zCKWUGMt+sY1WmPI8LTHnmBEoA6B93zNOQ7SVEMGbEJMPU4ixHYZ5mvrVhivlYzjYYExDA5pOCkbn455Tao8uZwKy+JwA8PXrS6nU82GMMT7tbNOalPJqtdKNcd7f3D9//bNPKamKNvvDs3WzEJIQdffw9G//6pcuLJ9++vm799cxxZ9//YuH+7txb+8e4rwECrxSu91uCElKdd7V29396/MTKQQBShhtjL692a3XzSdvr24+3m+2ejqGmFA3DDKWmlnT+BCRUlK5d4UyWknx3rdN+8J6C8ltJN988xOm8PVXX7we2pKjn2etNSEkl1RLbrsmxhQwU0YJIKHULr6W3PYdZlDSTHbSjRnWm/3zEQhpe2M6gym/TBGDSykB5cA5RwCQ0scXYzdpmgY4zd5ToD46QiqvjAJLJB+f91wKX/Lu/rpft2ks203fdcYFVzFTUmP0jFVWQTJWQuDACAgAjpWqps+V5JIopZvVAEAzYme6aZ41YaftYBoTUnTW51SSwpvH3f54/Nl621MOpDJ13goWgzdaYg4+Ovs4m77ZtMMcUvSBEkoq07pJpcQYSik+BCSJAqOE5lw5VK0UTyGnkoFSxZjWSgtVKwpRi3fHXAkhqQBWApIWIEI3T8fRNG03rF3wDzd3q/UKXWoaSal8uN8zztperTddjrRmrzXL2WKJbaOeHh4/OTvdrvrJjUKzkJPLwZi+NVwIoIx6bythpKZWC6YkYhWU23mKftaMnQyrXLJ3rm+3jDIKKJV8Pvgvv/4q0oSM//m/+tYvy+/+zb9Row8Hb8yayyo154pzKj/cPk7HZbPefvPjd599/nazWkOOiHCclqYfUmaase++/bZm/vbNsOwx5hxTZAAcoOTEtbDLzIFQApLyptEhkBgDpSx4j8CuH4+Esi8++3RoJC/RTUspNC/RBd/0rV0WO8+VgEtlGIyUlAEfj65tWkRUspnGIyO1bU3wVmjmXJBCUKQpk5QiYwwoUkFyDrViKQU4q7QwAZ3WQCGEKJiotaZSmOBAWAyOcbYSPSGVCyG1LDmdr1slCVB4iUphrUJIpdR+f+CyFIRaayVsXjyPmQAaozhTJVcpzePz8eb2qHthjECAyS4vaQ6XkmIi+JApvPrkTW8UutgpKjsVjnF2U6O4lAIoNMOGVBJmF2NstC6lxJATFmBEycaHkOIktVkWF1kyUteKyQdOoEhB+9XqJbzJGA0+SsaBUMGIUGJeXizlIsQEFdq2dyE+7q5jKkKaxcVasg356eG9Uub8/DSEPI4zViBInHWMlX7VHQ727dXZr37xxTff/lArtTZiebnwlkpojNU5TzmLyT897U5PzxrV/fTju7PT81prTuX07ARgub2+u3p91fcmpXm1XiOW9dDdfrj/5Ve/7VP4399/+NlXX7y7/bjYBUW9Ou+f7u6XcRZSHaed6vuDf4BZ/NZf/avLMj4+7V6fn+13ByD83c09kYxy48N8eb5lVOmm0Stp3SKUaLRGLKSUVduQCiWXtlH744FUIIRKpZ6epoy5Ynl1uWp1zX5hXBekhRAAUgkIpuyya5tWSWUU926mUsaajsex6Vrd9cu0pFqMliGlkjISQgBSxJjji72ilIQEKYVUUkUkQHkhUAoFWnMmACWglIpQUrDmgohIKAyNyTm/NInHw3R19RorXl+/61ebiqQS7Nqu5ISYhe4SAlQsJUspEWulNQUcj4fFxZCSDZFQ7lOSILuhTzFVrLkWKeWLStbG6WzTQyXTfBiargM9W4tABedSGa31ze1tiLVvOyUlwSw0yz74mLjUIcbkfQgh5wqxGGWcd3OeUXAqGPzX/+T3nfeEEKBQsUrOOWUvPYMKNWMhhJUKFERKMwXMueRSc0HGmDEmIaaC1x/vXl+9evv26v7+pmSsGRkFt7ih76Sk7dA87Y5SGE7yNHugQumGQD3s91IJraXzoRTkjMcYj9P4yadvd4/P3qXtyendw223blOt3/348YvLi8+uLsf50A+r43GkQFJcrA+18pPNilKKtbwIp7lqv//w8Hx/+/lnn+aUhTTX909t3wtBx2W5en15cTJ882d/ul5tZ1dmF4xmksnglqtXG6gCKB5nx5jklBJEIPWlklMIEGDWzkpJShhWOB6OMeerz94+7R6VrG8vTylW54u1qZQcYxRaUyCkEi0lZ3KZbcEADAjhlHHKoGCOMXEKQvCLszPvw9PzPhYsqTBKu64tJRfMUvIYIxcSCJWCL/PSmA5J9T7kklpj1k3rvRvtIhodggfEtu2cc0LIF3xNa7Pdbr77/pthvXEuVEJrrRRIDrFKQUplLz10SpWU3rmnQ6hEjPPIJG86pRvGJFz06+PDXilptH7ePVWC6/U6Y2mHLnp/fnb+4f27l+IrErZ41FJAxb5rD8ejD3FzcpK820/71brfDsPz07NUjQ2JIBVCeOdyyVzwEELIUTIpmKB28UoZpRtGhRCKU8mpQGCEsYJFSlly9rM77kZSxdPTGELWUrdKGyEgF1lLscvnr64+vbj48vXZ1abZatkJMbT6ZDOs+67k9PHDNaX8eJygiq5tGUcuEaAQSgEEAQACgsvFhml22+3pt998d7CTWjU3DzeNlhzLwPkX5xdfvX5Tk5Oc7J93T48jpU1Ftt6eT6FUENYGyLDsLc3s/uOjs6Fbn3+4P8yJ3D09SJp+8cUbjrh/ePiNr778Z//sv/2P/t7f/c3f+ssBSyP5yUoZLq4uTijElOtxnIBkBogYx3nPJFAJTBBb4s6OX//mLy/evMqQfZhMK9+8PovznhfcmJWhJvk8zUsGRFKEkYRWJFhrmafjvBy5YlwypbgQtCLm4AUFpZWU0khlpyW48JJPYYIRBsBpepl65OpCLjkLWnPwOYbgZwKFK1YAhRalFiCVU2IEX3Uto9SHTLlAUqlgVEAi6Wn/+Mknb7VSpSAQEFxwJjabLYPa9Q0htW3aksrxeZRMG1H6lpysmk7wgcsV8BNCcLIYs6DMukVowbWco9VGZmtFJdfvfqKMFQql1hepBBbknM3zUUnW96okL4TKKbZG/d7f+pudkiUEThnhMM7HUpLRCgAqgGnbzMiYAvwX//D3hGQVk5RcclUS5lQD5ozYtu3+OI3jRAiRQmnFhWRtaxAjVMyp5IwxJqlliLltDJbcKM0IZQCphgR0P07eWaMbzkXXdTTVSkmqxcdUEqaQjTKVeEr5PNuM1bTN0/MTALx9+3qeJ8y5FtI1fQjBLtP27HR/f/fFZ5/cHccYqgY5Zfe0e1jpXretj7kCHI+Hs/NTF/xkw353+MVvfNZoPe3D5enqfv8j9id/99/7/Ver7f/xv/yvxujru49MqSXmw/5RMjn0XS4xJgAKAhArsTEDpW3bclZT9JTyV+fnf/BP/rM//MP/4f/+4/+3HzaV1OhtLplzMXS9VjI4n3KaFmu6Rkjuva+V5VSQVMGp5pJxIKQui+OcD30/LzZm5IwJzg/Ho1LmZcj2sy8+/fGnH5XRsWTGmDGNdX5oZElh8VEIXZEoJYDWaV66rseUgJCcMuNCCEFItdZyLoVSyzIjViVlrUgZpBS9T4L/RcAOS6GsUk6x0lrheX+cF0cpb3pJKg1zCtFdvtpoKeIctdIVqlBytrOQTEuplAwhjMd5NfSIlQDFCs6HUmvByimVnAJWo/U4jVor5yPjPPjQtrpiGRfHhW4MLZTVWO3RjjmwVqpaWQXECv/Vf/J7pZKmbTOSHz9cS2UK1k6Stm2si4yLEMJmNQjOEYvWyrop+EUqLbiaJvuScFRavxgZJBNAKquotTiE9Ke//ubV+SsjmTGcQC2ZYcXDNKecGWWS8q5tx2WvtMZKN+vNzc3t4+7x69/4uZ3maR4558OwcS7f3d588eVbF2KZ/advrnbB390+E1+e7NitzKuTzY/vbyqh82I3q+Hi4nT3fMgIm7XpGn3YL8+Pzycn3dur89//3f8wQv3D//l/+tNf/9nXP/tyJaRi7MfnQ8pp3beN1kAglxK8N9oAI9HH1Wo1z7MxmjFeAgoAUuP1/V13cla52O+fLk43jZaLswyY0nqeF2edVDql0rXaxxAcMiYrQSlp+f/JRs4ZUBq8n8aZUOi6PsYopESsIYS27Rqt9odnoWTMWUpZK9ZSOaPOWQKCUKakglpSslqbw3Hs+54QggU5k7XWWpFxlnIOKZFKUkyCCUII4/Sl66e0Dj7OywwETGsyEmudczEjoYwvi2vW/PL8Al0htDBVSckcVY6ZAHLBAQihlQNQQjJCSIUS1FrHGELMBaiUMsbACMFSSy5McC4E1oq1eBcrocoI6yYlDSKcnvb5YMfR7lIc+oHEgLwKYfa7Pfw3//k/8D7f3T7Nizs9O+kGjZBbwWutFMQ0zSml9TBgiso0zgfOac4ZKyCSu7tHLXmjhFQKAAghWuuUEgGim+Zff/9+msLFyenQSt3C7EZG2pfZJ2PcLgumDISIrn35VFvmJac8DP3Q9x+ub07PTxiF/X58fHh++/b1at0+73YrbaRSHw7HP/qjf/nv/JW//v7+wydffnr7/oOUsuv6ZVkuLy9jDO/fvf/8szc2TB/ezaSaz77sTUsO9zNf0NVYNT15dbZZraoP82GacmWMaUFbo51dXoS9SwJC0HCanBVSlUpz5e9vbrXkf/mXX+UcU8ZSkQPRSgClzloXgjHt4XhsmpZL5WPgjATnKlIGEmvmrAJnUuqSi3POeb9aDYxB33bH49H7qIz+i4QzEkoZ44xQSkjFnHJMjdKxlExoiQkRtVJtZ2LwKSelda0158wZF1yFGAgh1tpf/eZvfvf9d7mUF29UxVpiIYQAAOMil1IQgw9NZ2qtKdWcykt9nVDSr3Vv9HwYhRSLd7VWXiHkTAC6rmMECBCChVFKCEsFAQhAxZpzwpAyUOjbxuhmvz/YELmUR2spwKY1KRbKOJZUapJCK9XEuBBfKOWZs8fn5+j9ZrMKhUzzwn+6vkshtdr84s2nQ9/EsJQYQ/CFgEuWc6FVk6LnFFJwSsqCSCmXUt7dPli3nJ29bZQkhKSUKGOL99OyLMuSa91P89Xl6+263676VBxwiqnGWCljUqrxcORcSinvH59W/VBrEZIOw0oK+fBwL4XgjN3cXkefTk42jMHd3Y1UAglpupYFZwazWnfneRWDG63dCiY4OdsOpIQS48Xp5vx0/fgIbVN00wjBbj/cCtEeDP3s6tM0HjdMlGlCxgIjJNWSUqWiVowxpZx1YzihpMISPCKnorl7eBwntz5bn50OIJmkYpoOiquXXf/RxxRT05gYCGMmRsw5KCMpRdY03he/OKC171cZSwwxxuJcWq82hCAArRUQK+cieM+YMLqNMcaYhJAAJJeCpZxst68uzv+vf/7Hp69ePU6jZEyI5mUF3red0vpwPHLGOBcpJ0qpd55Sdv3hI8lYESXnNnhSa6PaWmvMmQAJKVDGuRJYPFbIKW+22/14BEDKiABiBOvO1vcPOywwDCsOKGPECoKyl6lWTElptRq62483yqhUIuOcS0lCyilPYK31SqoGKAFgiCcnW1pKqZhd0EYKIb2LKTsg1dLaNyo9Hma7ZMnXCFBz22n4p//ob3NBc0laK+9TjAVAtiz+jb/+O7/+9vun3TMAACGUgpLqpWRVCaScnvf7V1dXKaaScs455bwa+lhK5ZQUwpBwzRWjtOD4fJCqEUrkGglhMeW7x6fjaKlUnPNVozfrlfXLMPSI5XgYF7ucn7/aH3YvL44IjMHZ+er99SPN/Kf3755iuLp8rXwhEDIHytiXn74hJVbEp6ddKezifA0ErTcPu1uhM60SMjs7P/l4txs63bWckgwAyrQpoXOhlKSUitFzIWMpldC1ZhXh8TiHUoGxeZ4uz86GVgvA5F0FmHzKhW7Xw9NuR4BRQoZVF5yjlEjJgbycHKN1LmX0LppWU4pQgQInldVKCEEC2HXN4TByLghAwRJD4pyHEAlWzpluNOc85wBAtJRHZ7HW15evOm1+/PEnbRpElFJ5bwkhBUsuSIFRRillGAtjjFIoteRcUs0hBs11zqUCiSnHlHTbhBA6KZUyD4/PqWSpVdNpZUQLYtUqwWFa/O4YN9uTZXzknB3HWUlZSkFCCiE+x6vT82QXKoUPIeUMwEsslVQQXAvprDVS5pzneaaMgeEpFCU1ABojU8jWxgzltBuO47SPTitTQg45N7opOfMcQ0nAOLOLp0DXQ1sJ1ET+xZ/8mhAA4IilEhz6vpSKiJUCJXQ87i7OzjSnNZPDfOi7VgrOGDIsKWQlzbxYiRGV+PHdjXPp5PwsPD0nm6Vung/jw+7p4tWFnaazs21APNpls9nc3N6lnEP2umlHFx5306ev31bE++uP6/XqyOXj3U6YZqZQqSRMT/Ww0fT87EwyRcZj23U/PB8fbTaS3h1HJqDk+O727s2bV5++uXq4vbm5u/Mls1hDxK5pOCPzvNuenpJYQvBUUNMbxMp87BsjaqWCD5152B207njXacFv7h67rhOMCgYEcbPqas1CyFpBaVmwAKdCCCGkXRyFZH3IhFZKdKsAM1TKhOSMxhgYp4wJqKykyoWOKWFF0zQho8+JMKKlpkBS9Fp2QqmSESptZQtQScyxOsVoiQGAO7fEErVsSyIx5PPzbS7xcHhWUqWCDFnwqRKCBDXXKS7rzenj8/Tdj7dt3319cSlhz5ATQkMOpmuUkLKSFRgtKSE11cIEXw0MMTKlpmWxMVXG2sY4t6y7drO9+v6H94KZtCxGq5oLASSkYq3W2ZJrjIXQAoLfLh4I/MHf+Xf/zz/6F4fJdn0vpOCUl5gOx0kKrhrdIkabKOHJxkOcjFE85FpKglq7rjVG55BSCrmCjyXlXCsyCk2jcwpC8FQLBe6s356cAdC7h8O8TJvNljLGKIRQ5tlKbR6fHru2YxzmedZSvH39iffusN892LhV8m58/Ozzq/XQbLpTySHEahpzc33NgA7rTc5ZNubDT++H1doh3tzfZyDLYc/axqa6HrSi9PNP34aQgItQChXNu3cfeXQ//2qdA3ZCr5pGcZBGp0jenF188up1zeXx7unVq1et6QArJVArVCorh7vH54hFSgWE1QiAlRUx7e3DPk3WVZKlZqdnjehQQL68WBNgyXvO+KprX/xEtCLjrNUi56i5BAIlpRLdFLw0JsVEKJ2mcbteScaPx2kYuhdpsJIaC87zXCoKKQlhOUaSkQLlXCilonOVQIhRKxVTygVN08UU7x53/dAFQkutSokcixTSOW9t0Fodj0cCJYREQBMsAIiAIbhUomk0k2aa7LI4IUTf9cnHYF2BqAEvz09zTEBIDCmo3Pet4MTGhRBqtNofp+Px0A+rZmNSyTVXBiJGjB45EwxqwozIKYWUckyRCrYaVE2VEhrjdLa9eH25bU23++kej+GkXfmY5mSB0EJ4y1dhLI7kYbW6s7v99Fwpaxicn17CP/rd39FGI2bOqBJCKlmx+FQIECkEIQikaiWFECHYpmke7p+9z0qbcZwZF5vN0HWakBq9c95xIaTSznnOuHMzpQyRUhDLMgEjqjOSy8Ph8PryytmFEjJNRyY5Y2Ka5qbpr69vjWlzQcV423W//uF7rk3TNsm5zuhutc4lJj+v+uHdTx9Ozy66ofv+px+BiqvTi7ZppmVijLZKYkqz8zEU692r1+ezna0N2+2JIgQocM59DMoY58PN7W0F3mgTZnfYH7746qv7w/P729tGycuLcy0FZ+V01bdSKAqZYsoFKjB4QTml8z7mJAXvuy7FwLkgBI7HGQkSSq0LqSDjAoA0jckpQgWjZUopBLtZnwCBebaEVcZ4zmVZ/DIv6/VG66aSYpfZGG0aFWN0zrdtF13QUpdaa8VQcsqJMqYkp4TEWIQQtZJlmaXkX/zs8+vbG2ttzvjiLKfAYsohus4YAHYcbSy4HRpBy1T80HUr002HaZ6Xdr0OJZ11LcEgtFis49xUQp+fn16/eo0I19fXpJJSK+M8pDz0LZCccxFCAqU+xpxKwSyVkFzN49g0Ghg4X0JINTsuDOUylWSMSj4fj3bO4fL8LHr39Hj/q1/9JVLJ835/e7eTUsJ/+Y//A+dsCIFxVitBglqbzhhvHROMUkIqqRWAQMqpIM7zcnF+eXd3T4Cs1kOKzmihpHCLffvmbanw4eMNF2Ka575rKWPf/XC9zO7s8pTT/Grd5oSmaa1LqmldiiFlO45CCB8TUB5jZpRxynfzoZRCCq76HgAaowglwAgQlEJQwsaD5ZwBr5+82f61v/Y7/91//z9K1SImrAlzbUy3e94Pw6ppNIHkvU2FEiIweGAkIUopBOclRsH5/eP+8vJqWmwqNaQIHLph+Pqzs/vbj1KIoe1KKrQyUsEXjxWjT4wwH4JQqmChQClQzFlwrk37zQ8/IrAKpDH6cDgOw0prpaXIJRqjl8karQiptRajm+AC43I3jVgKqTUXNEojIha01m62G8ZZ17WH4yHESAiVADWXTIiUKuaUUlr1bSrFhdh3nQ9hvVpZ5xDrq8vzlOzH65sKlDN5sj077MZcsOl4sNNqWPmIH++eKKnrvq0CN6vVMs0VUXChjLbeccJrzVJSaz2AWGzQRjLKxnEaht46xyiPIWw221oLg0opv3t4EEoppcdpygW7vsVcgdRhaI/7PWeSMjWXuVZKCfPeNkbNY/C+0JadnqzeXJx/8+d/dnVxfnF29t03337zPBGl+eFwv15v21bXSu8fH1brDQBEF40SMQYqVEzZBet9mZbF6IYx/vD03A0do0RQUggJLkPhWq1y5rvd/ul+jDl++sVbH+LT3X2t9fMvPpGKSyWAcKDRJSxQXVgqQEqOM9l2/ftvv91stsNm9fHDh2VexLq9evPm+eaRAtCKzk7dtpv34+vLT3793Y/zEgFoZ2TfycvVUI+PG8V9CVDL0KhYIMREsCpONcWTk83tXUjAnE9Iq9GqevuLn38hhfj+2285pVevN8DSiWmmadpQfbZeh2XOh+my2x6nY2HxhSNKGVPJjWkKg+gjF0o3zWG/RwIEoaTMef3XP3zjSqlckopI6rBeG6UZQMlJcmrnCYDPy0JI1Uou1pWM+6cd4ZwQQoG2bZdSPo6TUkJIkVJCUnf7vbPWNEZIXXLMgCkXQrFZdV3Teju7ObVdN82z977rupyzFOz6w81xP7V9Y1oTgp3Gp1KCVooSgiVaOyZkggNQ4WJaSV0SplyGVS+4YFD9kpHwmDCXnBBeFslcSAKk6boKIKTEUoCSEJ3gPJW62EMlBCifZgcgEa1plF28XZZhMJQDBZjGA2IGynTbMCXHeXGpZABuQ1byvX13sj0Z5/kwz2ZYv07EKMVF0/qYU/RKiFdn5zmlRolSmfV7UokbQRrZb1b2cTnZyK7R13fP0hgfE6YoQSLAEt18NxHCb+7+eLPuzy8u8pwfDuO79w9ays9fnxieO2OOk3te0nQcN5tV13Fr5/OLq6eHceg6O8/b9bA5WT3udtKI81efDpo/7paYKllzb92qP9k/P3Smu30c93OeZ/vLX3xOaxRK/G9/9P9xwnU/HMd93ynKTU75cb9vtOGKASecSQB1ODwN6xVDImtuRf1bv/3L+8enb/+Vp82gkW5Wq+k4MqVOTk8z4m6MPCvrA1I+2di0XXAu5ayl8VMADgjImSgJrU1EMGBMCHmcHKimBt9oY5RoFCulMFKVFFgqp9z0zXGZmBIUaK1AEJYUaas14zmXmmE62lRxClH0w+vz0+eHR6O0MXqRUghhreUEKmFc875rGyEl5zaV1ap3IVRS1+sVBdK3SgoaBPVR2uB00zCqUkpcKyp49EGoJpWKhKxX/c3dwzAMx9m6h6dh6HOqOXoAQoV68clRIklNhBDGIPoIUDml3jrG2dD14zjGGAnAYpeMKLsm5YxQAbFtDCHFaGkn8vy0dKvt/cPj4/P+9OzENER1zB0i5220h27TFF+50SWGXMv6ZPP48Hw8+lop5RL+4O//HefnYTA5B844Y2qZHbAgeBt8lopMUwgeCCt9y6JPIROmRIzu4vQs+3xc7MEuV5fn+/2ha9u2aWIJJYVWde9v74dhE+xilLi5f2BKfnZ17l2GyijgtOxPz08W6wrKcRqllDlFwbkUglG6FGtt4Zx3A328PzRi0/WyZkeYuH16PN2sJODr8wtS2Z/86Z9fvn4tTBuCd9PhdHtymOw42qvLSwpeKZ5ibpreBs+lcM4LwE1ncvDAGDKBhCYfGFDJhZQipkQAXPBCSyEFYp2nmTOeUqYUOFfOhgrovTNNuz+MnEvRSMyJIYUKNqWEuOr6WnJM4YUsXq36GPzLldm50AxdDKmkkkIAzjOpvDLn/WGeuFRt13AGHKDVOpecU+r61nuvlJrmSQAviKY3OcZsg1S6EFIJeOdjjFLKtmsIqdYesfLFQ4xh6IeSI5BqlAQgWDMi2Bi0afbHaVqW09MzZ4+rYagVpWAEqxCMAkzTRAG0bJ52Oy5113bTbBnUrmsrIbmUFIPkouScEYHxxS5nF+c55eNhL4XcrIeQbcl0nJy1kUnJJQ8xDEZxAVAxRey69e3DPTCgOTHgbWP6zsx2QkKx8B9uHyMi/NP/+G8DVBsWqVRK9P27h6fH48n5xlkIwX/xxXbcj9Hh+YWRiknVt93qh59+ePv2ajweH+/3pum7ofXzs1aq61bOhW7dHHaPm25VaHXOG9U6mxKpVFC3zE8P4+nmnDFCOQpNrXWVQdu0mItgTAixe3x88+bNzdNzsHHdqovXmz/5sx/H5/L1l68IJhvKbjpcnJ8s86Fvu0r1OO6vLi+eDtOyLKtGC06FbpbFvzrdAuSUYwipVmaDL1ikkJyR1mgsmXORCwKwHJKSMsUgpQAArJgrVs5yzpiy0Sb4iFArgPcJsaYUtZaUcsSKSAomJXlNlRCKlGCtkkJJmVDKKDDOlJaYCxfUW89Aci1/fP+OETg/OZdaPeyej4eZMtoOTdM1gjGjZM0FMTaNybkAIYzxUjDEWHLmSvrghqbVXB4PE3K22HR4Hqfp+Obta6Wk8w4RKWejXRqtW2OsnQVjfdfGGGMqtUIqJaQccwHKuJCbTtVSXrp9SqoYA+ccKVZCoouCK9O0s3UuJcU5B+qjd94pKRujSUYmxdFbRkAKjqU0WjEKuaTDPBmzOhz9bF1MoV93J5uhZZAyuhBLQVIrFgRCVcuV0If9MUbPJONSc9mkEKfjgXtnKWdA+O55qpTJjv+lT3/j3cfHMR+2J5vb54WT+tnPLiStvuDeLo/7MaWYYjw870+2J/v9rI24vNqSSvY7+3ywj/MsaOXgXVnarkkpEayYA2PSLbUW6LtumnfaNA/3u/Vqu1pzo7RdrPfxYfdMmfzupw9Iqa4izf7pbh8Tm4NdnZjru/H24I7jQpjWXKRCgOLJdj0f9wzE5dmZoFhrziS2rehbVYG5gJTxVMjFZmXdbBeLhBaASpmLESrEGICLeZooIa3gjJKQE2UUQ5RMgOBaaCAUKb2+v1NSaSWBFsZejCzyhaTJoSyTbVcdkzJFHwpKzrBgBVawvPBy0WFjTPL1cfckjQEkTIjDeIw5mvVASdWCi1IUY8n5yqhkHDNSoLUSAFZKLrn4lAhiivFsc06wPj7v2/X26XhcYrj45LXu2/uH+4rY9yutdc7IKYTgtFYAMC0LADDBhVB+f+jbRmszL65ru1ri7G0IMZcsZAJKMcTNdmuXJWdCoZaMj09PumtphcN8aPquApiupYQwQn1Ji19OV5vNMLh5GfrO2aVkpCCWJVgbgk+rdXdxvlaSbHTz4eMTpZLIEpPbrjd2Dm2rnA1SqrZtKYeU8mF/7Dt9sun+DX7JponG4t+5AAAAAElFTkSuQmCC",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 Predictions:\n",
      "1. racer: 80.76%\n",
      "2. sports_car: 6.23%\n",
      "3. pickup: 4.22%\n",
      "4. tow_truck: 2.58%\n",
      "5. car_wheel: 1.95%\n"
     ]
    }
   ],
   "source": [
    "ROOT = \"https://data.heatonresearch.com/data/t81-558/images/\"\n",
    "\n",
    "classify_image(ROOT+\"soccer_ball.jpg\")\n",
    "classify_image(ROOT+\"race_truck.jpg\")"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "anaconda-cloud": {},
  "colab": {
   "collapsed_sections": [],
   "name": "Copy of t81_558_class_06_3_resnet.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3.9 (torch)",
   "language": "python",
   "name": "pytorch"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
